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README.md
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---
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title: Druggen
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sdk: gradio
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app_file: gradio_app.py
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emoji: 💊
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colorFrom: red
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colorTo: green
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---
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# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks
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<p align="center">
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<a href="https://github.com/HUBioDataLab/DrugGEN/files/10828402/2302.07868.pdf"><img src="https://img.shields.io/badge/paper-report-red"/></a>
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<a href="http://www.gnu.org/licenses/"><img src="https://img.shields.io/badge/License-GPLv3-blue.svg"/></a>
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</p>
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<!--PUT HERE SOME QUALITATIVE RESULTS IN THE ASSETS FOLDER-->
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<!--YOU CAN PUT ALSO IN THE GIF OR PNG FORMAT -->
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<!--<p float="center">
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<img src="assets/sample1.png" width="49%" />
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<img src="assets/sample2.png" width="49%" />
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</p>-->
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## Updated Pre-print!
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**Please see our most up-to-date document (pre-print) from 15.02.2023 here:** [2302.07868.pdf](https://github.com/HUBioDataLab/DrugGEN/files/10828402/2302.07868.pdf), [arXiv link](https://arxiv.org/abs/2302.07868)
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## Abstract
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Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, have been developed with the purpose of picking completely new samples from a partially known space. Generative models offer high potential for designing de novo molecules; however, in order for them to be useful in real-life drug development pipelines, these models should be able to design target-specific molecules, which is the next step in this field. In this study, we propose DrugGEN, for the de novo design of drug candidate molecules that interact with selected target proteins. The proposed system represents compounds and protein structures as graphs and processes them via serially connected two generative adversarial networks comprising graph transformers. DrugGEN is trained using a large dataset of compounds from ChEMBL and target-specific bioactive molecules, to design effective and specific inhibitory molecules against the AKT1 protein, which has critical importance for developing treatments against various types of cancer. On fundamental benchmarks, DrugGEN models have either competitive or better performance against other methods. To assess the target-specific generation performance, we conducted further in silico analysis with molecular docking and deep learning-based bioactivity prediction. Results indicate that de novo molecules have high potential for interacting with the AKT1 protein structure in the level of its native ligand. DrugGEN can be used to design completely novel and effective target-specific drug candidate molecules for any druggable protein, given target features and a dataset of experimental bioactivities. Code base, datasets, results and trained models of DrugGEN are available in this repository.
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Our up-to-date pre-print is shared [here](https://github.com/HUBioDataLab/DrugGEN/files/10828402/2302.07868.pdf)
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<!--Check out our paper below for more details
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> [**DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks
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**](link here),
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> [Atabey Ünlü](https://tr.linkedin.com/in/atabeyunlu), [Elif Çevrim](https://www.linkedin.com/in/elifcevrim/?locale=en_US), [Ahmet Sarıgün](https://asarigun.github.io/), [Heval Ataş](https://www.linkedin.com/in/heval-atas/), [Altay Koyaş](https://www.linkedin.com/in/altay-koya%C5%9F-8a6118a1/?originalSubdomain=tr), [Hayriye Çelikbilek](https://www.linkedin.com/in/hayriye-celikbilek/?originalSubdomain=tr), [Deniz Cansen Kahraman](https://www.linkedin.com/in/deniz-cansen-kahraman-6153894b/?originalSubdomain=tr), [Abdurrahman Olğaç](https://www.linkedin.com/in/aolgac/?originalSubdomain=tr), [Ahmet S. Rifaioğlu](https://saezlab.org/person/ahmet-sureyya-rifaioglu/), [Tunca Doğan](https://yunus.hacettepe.edu.tr/~tuncadogan/)
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> *Arxiv, 2020* -->
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<!--PUT THE ANIMATED GIF VERSION OF THE DRUGGEN MODEL (Figure 1)-->
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</p>
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**Fig. 1.** **(A)** Generator (*G1*) of the GAN1 consists of an MLP and graph transformer encoder module. The generator encodes the given input into a new representation; **(B)** the MLP-based discriminator (*D1*) of GAN1 compares the generated de novo molecules to the real ones in the training dataset, scoring them for their assignment to the classes of “real” and “fake” molecules; **(C)** Generator (*G2*) of GAN2 makes use of the transformer decoder architecture to process target protein features and GAN1 generated de novo molecules together. The output of the generator two (*G2*) is the modified molecules, based on the given protein features; **(D)** the second discriminator (*D2*) takes the modified de novo molecules and known inhibitors of the given target protein and scores them for their assignment to the classes of “real” and “fake” inhibitors.
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## Transformer Modules
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Given a random noise *z*, **the first generator** *G1* (below, on the left side) creates annotation and adjacency matrices of a supposed molecule. *G1* processes the input by passing it through a multi-layer perceptron (MLP). The input is then fed to the transformer encoder module [Vaswani et al., (2017)](https://arxiv.org/abs/1706.03762), which has a depth of 8 encoder layers with 8 multi-head attention heads for each. In the graph transformer setting, *Q*, *K* and *V* are the variables representing the annotation matrix of the molecule. After the final products are created in the attention mechanism, both the annotation and adjacency matrices are forwarded to layer normalization and then summed with the initial matrices to create a residual connection. These matrices are fed to separate feedforward layers, and finally, given to the discriminator network *D1* together with real molecules.
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**The second generator** *G2* (below, on the right side) modifies molecules that were previously generated by *G1*, with the aim of generating binders for the given target protein. *G2* module utilizes the transformer decoder architecture. This module has a depth of 8 decoder layers and uses 8 multi-head attention heads for each. *G2* takes both *G1(z)*, which is data generated by *G1*, and the protein features as input. Interactions between molecules and proteins are processed inside the multi-head attention module via taking their scaled dot product, and thus, new molecular graphs are created. Apart from the attention mechanism, further processing of the molecular matrices follows the same workflow as the transformer encoder. The output of this module are the final product of the DrugGEN model and are forwarded to *D2*.
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<!--PUT HERE 1-2 SENTECE FOR METHOD WHICH SHOULD BE SHORT Pleaser refer to our [arXiv report](link here) for further details.-->
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<!-- - supports both CPU and GPU inference (though GPU is way faster), -->
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<!-- ADD HERE SOME FEATURES FOR DRUGGEN & SUMMARIES & BULLET POINTS -->
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<!-- ADD THE ANIMATED GIF VERSION OF THE GAN1 AND GAN2 -->
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| First Generator | Second Generator |
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|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------|
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## Model Variations
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- **DrugGEN-Prot** (the default model) is composed of two GANs. It incorporates protein features to the transformer decoder module of GAN2 (together with the de novo molecules generated by GAN1) to direct the target centric molecule design. The information provided above belongs to this model.
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- **DrugGEN-CrossLoss** is composed of only one GAN. The input of the GAN1 generator is the real molecules (ChEMBL) dataset (to ease the learning process) and the GAN1 discriminator compares the generated molecules with the real inhibitors of the given target protein.
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- **DrugGEN-Ligand** is composed of two GANs. It incorporates AKT1 inhibitor molecule features as the input of the GAN2-generator’s transformer decoder instead of the protein features in the default model.
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- **DrugGEN-RL** utilizes the same architecture as the DrugGEN-Ligand model. It uses reinforcement learning (RL) to avoid using molecular scaffolds that are already presented in the training set.
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- **DrugGEN-NoTarget** is composed of only one GAN. This model only focuses on learning the chemical properties from the ChEMBL training dataset, as a result, there is no target-specific generation.
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## Files & Folders
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We provide the implementation of the DrugGEN, along with scripts from PyTorch Geometric framework to generate and run. The repository is organised as follows:
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```data``` contains:
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- **Raw dataset files**, which should be text files containing SMILES strings only. Raw datasets preferably should not contain stereoisomeric SMILES to prevent Hydrogen atoms to be included in the final graph data.
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- Constructed **graph datasets** (.pt) will be saved in this folder along with atom and bond encoder/decoder files (.pk).
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```experiments``` contains:
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- ```logs``` folder. Model loss and performance metrics will be saved in this directory in seperate files for each model.
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- ```tboard_output``` folder. Tensorboard files will be saved here if TensorBoard is used.
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- ```models``` folder. Models will be saved in this directory at last or preferred steps.
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- ```samples``` folder. Molecule samples will be saved in this folder.
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- ```inference``` folder. Molecules generated in inference mode will be saved in this folder.
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**Python scripts:**
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- ```layers.py``` contains **transformer encoder** and **transformer decoder** implementations.
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- ```main.py``` contains arguments and this file is used to run the model.
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- ```models.py``` has the implementation of the **Generators** and **Discriminators** which are used in GAN1 and GAN2.
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- ```new_dataloader.py``` constructs the graph dataset from given raw data. Uses PyG based data classes.
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- ```trainer.py``` is the training and testing file for the model. Workflow is constructed in this file.
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- ```utils.py``` contains performance metrics from several other papers and some unique implementations. (De Cao et al, 2018; Polykovskiy et al., 2020)
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## Datasets
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Three different data types (i.e., compound, protein, and bioactivity) were retrieved from various data sources to train our deep generative models. GAN1 module requires only compound data while GAN2 requires all of three data types including compound, protein, and bioactivity.
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- **Compound data** includes atomic, physicochemical, and structural properties of real drug and drug candidate molecules. [ChEMBL v29 compound dataset](data/dataset_download.sh) was used for the GAN1 module. It consists of 1,588,865 stable organic molecules with a maximum of 45 atoms and containing C, O, N, F, Ca, K, Br, B, S, P, Cl, and As heavy atoms.
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- **Protein data** was retrieved from Protein Data Bank (PDB) in biological assembly format, and the coordinates of protein-ligand complexes were used to construct the binding sites of proteins from the bioassembly data. The atoms of protein residues within a maximum distance of 9 A from all ligand atoms were recorded as binding sites. GAN2 was trained for generating compounds specific to the target protein AKT1, which is a member of serine/threonine-protein kinases and involved in many cancer-associated cellular processes including metabolism, proliferation, cell survival, growth and angiogenesis. Binding site of human AKT1 protein was generated from the kinase domain (PDB: 4GV1).
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- **Bioactivity data** of AKT target protein was retrieved from large-scale ChEMBL bioactivity database. It contains ligand interactions of human AKT1 (CHEMBL4282) protein with a pChEMBL value equal to or greater than 6 (IC50 <= 1 µM) as well as SMILES information of these ligands. The dataset was extended by including drug molecules from DrugBank database known to interact with human AKT proteins. Thus, a total of [1,600 bioactivity data](data/filtered_akt_inhibitors.smi) points were obtained for training the AKT-specific generative model.
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<!-- To enhance the size of the bioactivity dataset, we also obtained two alternative versions by incorporating ligand interactions of protein members in non-specific serine/threonine kinase (STK) and kinase families. -->
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More details on the construction of datasets can be found in our paper referenced above.
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## Getting Started
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DrugGEN has been implemented and tested on Ubuntu 18.04 with python >= 3.9. It supports both GPU and CPU inference.
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git clone https://github.com/HUBioDataLab/DrugGEN.git
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```
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<!--## Running the Demo
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You could try Google Colab if you don't already have a suitable environment for running this project.
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It enables cost-free project execution in the cloud. You can use the provided notebook to try out our Colab demo:
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## Training
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### Setting up environment
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You can set up the environment using either conda or pip.
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Here is with conda:
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```
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### Starting the training
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python DrugGEN/main.py --submodel="CrossLoss" --mode="train" --raw_file="DrugGEN/data/chembl_train.smi" --dataset_file="chembl45_train.pt" --drug_raw_file="DrugGEN/data/akt_train.smi" --drug_dataset_file="drugs_train.pt" --max_atom=45
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```
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** Explanations of arguments can be found below:
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```bash
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Model arguments:
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--submodel SUBMODEL Choose the submodel for training
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--act ACT Activation function for the model
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--z_dim Z_DIM Prior noise for the first GAN
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--max_atom MAX ATOM Maximum atom number for molecules must be specified
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--lambda_gp LAMBDA_GP Gradient penalty lambda multiplier for the first GAN
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--dim DIM Dimension of the Transformer models for both GANs
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--depth DEPTH Depth of the Transformer model from the first GAN
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--heads HEADS Number of heads for the MultiHeadAttention module from the first GAN
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--dec_depth DEC_DEPTH Depth of the Transformer model from the second GAN
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--dec_heads DEC_HEADS Number of heads for the MultiHeadAttention module from the second GAN
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--batch_size BATCH_SIZE Batch size for the training
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--epoch EPOCH Epoch number for Training
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--warm_up_steps Warm up steps for the first GAN
|
217 |
-
--g_lr G_LR Learning rate for G
|
218 |
-
--g2_lr G2_LR Learning rate for G2
|
219 |
-
--d_lr D_LR Learning rate for D
|
220 |
-
--d2_lr D2_LR Learning rate for D2
|
221 |
-
--n_critic N_CRITIC Number of D updates per each G update
|
222 |
-
--beta1 BETA1 Beta1 for Adam optimizer
|
223 |
-
--beta2 BETA2 Beta2 for Adam optimizer
|
224 |
-
--clipping_value Clipping value for the gradient clipping process
|
225 |
-
--resume_iters Resume training from this step for fine tuning if desired
|
226 |
-
Dataset arguments:
|
227 |
-
--features FEATURES Additional node features (Boolean) (Please check new_dataloader.py Line 102)
|
228 |
-
```
|
229 |
-
|
230 |
-
<!--ADD HERE TRAINING COMMANDS WITH EXPLAINATIONS-->
|
231 |
-
|
232 |
-
|
233 |
-
|
234 |
-
|
235 |
-
## Molecule Generation Using Trained DrugGEN Models in the Inference Mode
|
236 |
-
|
237 |
-
|
238 |
-
- First, please download the model weights of trained model, e.g., [DrugGEN-Prot](https://drive.google.com/drive/folders/19knQAtpieSamaxB4L5ft8bFiCVikBFDS?usp=share_link) and place it in the folder: "DrugGEN/experiments/models/".
|
239 |
-
- After that, please run the code below:
|
240 |
-
|
241 |
-
|
242 |
-
```bash
|
243 |
-
|
244 |
-
python DrugGEN/main.py --submodel="Prot" --mode="inference" --inference_model="DrugGEN/experiments/models/{Chosen model name}"
|
245 |
-
```
|
246 |
-
|
247 |
-
- SMILES representation of the generated molecules will be saved into the file: "DrugGEN/experiments/inference/{Chosen submodel name}/denovo_molecules.txt".
|
248 |
-
|
249 |
-
|
250 |
-
|
251 |
-
|
252 |
-
## Results (De Novo Generated Molecules of DrugGEN Models)
|
253 |
-
|
254 |
-
- SMILES notations of 50,000 de novo generated molecules from DrugGEN models (10,000 from each) can be downloaded from [here](results/generated_molecules).
|
255 |
-
- We first filtered the 50,000 de novo generated molecules by applying Lipinski, Veber and PAINS filters; and 43,000 of them remained in our dataset after this operation ([SMILES notations of filtered de novo molecules](results/generated_molecules/filtered_all_generated_molecules.smi)).
|
256 |
-
- We run our deep learning-based drug/compound-target protein interaction prediction system [DEEPScreen](https://pubs.rsc.org/en/content/articlehtml/2020/sc/c9sc03414e) on 43,000 filtered molecules. DEEPScreen predicted 18,000 of them as active against AKT1, 301 of which received high confidence scores (> 80%) ([SMILES notations of DeepScreen predicted actives](results/deepscreen)).
|
257 |
-
- At the same time, we performed a molecular docking analysis on these 43,000 filtered de novo molecules against the crystal structure of [AKT1](https://www.rcsb.org/structure/4gv1), and found that 118 of them had sufficiently low binding free energies (< -9 kcal/mol) ([SMILES notations of de novo molecules with low binding free energies](results/docking/Molecules_th9_docking.smi)).
|
258 |
-
- Finally, de novo molecules to effectively target AKT1 protein are selected via expert curation from the dataset of molecules with binding free energies lower than -9 kcal/mol. The structural representations of the selected molecules are shown in the figure below ([SMILES notations of the expert selected de novo AKT1 inhibitor molecules](results/docking/Selected_denovo_AKT1_inhibitors.smi)).
|
259 |
-
|
260 |
-

|
261 |
-
Fig. 2. Promising de novo molecules to effectively target AKT1 protein (generated by DrugGEN models), selected via expert curation from the dataset of molecules with sufficiently low binding free energies (< -9 kcal/mol) in the molecular docking experiment.
|
262 |
-
|
263 |
-
|
264 |
-
|
265 |
-
|
266 |
-
## Updates
|
267 |
-
|
268 |
-
- 15/02/2023: Our pre-print is shared [here](https://github.com/HUBioDataLab/DrugGEN/files/10828402/2302.07868.pdf).
|
269 |
-
- 01/01/2023: Five different DrugGEN models are released.
|
270 |
-
|
271 |
-
|
272 |
-
|
273 |
-
|
274 |
-
## Citation
|
275 |
-
```bash
|
276 |
-
@misc{nl2023target,
|
277 |
-
doi = {10.48550/ARXIV.2302.07868},
|
278 |
-
title={Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks},
|
279 |
-
author={Atabey Ünlü and Elif Çevrim and Ahmet Sarıgün and Hayriye Çelikbilek and Heval Ataş Güvenilir and Altay Koyaş and Deniz Cansen Kahraman and Abdurrahman Olğaç and Ahmet Rifaioğlu and Tunca Doğan},
|
280 |
-
year={2023},
|
281 |
-
eprint={2302.07868},
|
282 |
-
archivePrefix={arXiv},
|
283 |
-
primaryClass={cs.LG}
|
284 |
-
}
|
285 |
-
```
|
286 |
-
|
287 |
-
Ünlü, A., Çevrim, E., Sarıgün, A., Çelikbilek, H., Güvenilir, H.A., Koyaş, A., Kahraman, D.C., Olğaç, A., Rifaioğlu, A., Doğan, T. (2023). Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks. *arXiv preprint* arXiv:2302.07868.
|
288 |
-
|
289 |
-
|
290 |
-
|
291 |
-
|
292 |
-
|
293 |
-
## References/Resources
|
294 |
-
|
295 |
-
In each file, we indicate whether a function or script is imported from another source. Here are some excellent sources from which we benefit from:
|
296 |
-
<!--ADD THE REFERENCES THAT WE USED DURING THE IMPLEMENTATION-->
|
297 |
-
- Molecule generation GAN schematic was inspired from [MolGAN](https://github.com/yongqyu/MolGAN-pytorch).
|
298 |
-
- [MOSES](https://github.com/molecularsets/moses) was used for performance calculation (MOSES Script are directly embedded to our code due to current installation issues related to the MOSES repo).
|
299 |
-
- [PyG](https://github.com/pyg-team/pytorch_geometric) was used to construct the custom dataset.
|
300 |
-
- Transformer architecture was taken from [Vaswani et al. (2017)](https://arxiv.org/abs/1706.03762).
|
301 |
-
- Graph Transformer Encoder architecture was taken from [Dwivedi & Bresson (2021)](https://arxiv.org/abs/2012.09699) and [Vignac et al. (2022)](https://github.com/cvignac/DiGress) and modified.
|
302 |
-
|
303 |
-
Our initial project repository was [this one](https://github.com/asarigun/DrugGEN).
|
304 |
-
|
305 |
-
|
306 |
-
|
307 |
-
|
308 |
-
## License
|
309 |
-
Copyright (C) 2023 HUBioDataLab
|
310 |
-
|
311 |
-
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
|
312 |
-
|
313 |
-
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
|
314 |
-
|
315 |
-
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
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|
app.py
DELETED
@@ -1,189 +0,0 @@
|
|
1 |
-
import streamlit as st
|
2 |
-
import streamlit_ext as ste
|
3 |
-
|
4 |
-
from trainer import Trainer
|
5 |
-
import random
|
6 |
-
from rdkit.Chem import Draw
|
7 |
-
from rdkit import Chem
|
8 |
-
from rdkit.Chem.Draw import IPythonConsole
|
9 |
-
import io
|
10 |
-
from PIL import Image
|
11 |
-
|
12 |
-
class DrugGENConfig:
|
13 |
-
submodel='CrossLoss'
|
14 |
-
act='relu'
|
15 |
-
z_dim=16
|
16 |
-
max_atom=45
|
17 |
-
lambda_gp=1
|
18 |
-
dim=128
|
19 |
-
depth=1
|
20 |
-
heads=8
|
21 |
-
dec_depth=1
|
22 |
-
dec_heads=8
|
23 |
-
dec_dim=128
|
24 |
-
mlp_ratio=3
|
25 |
-
warm_up_steps=0
|
26 |
-
dis_select='mlp'
|
27 |
-
init_type='normal'
|
28 |
-
batch_size=128
|
29 |
-
epoch=50
|
30 |
-
g_lr=0.00001
|
31 |
-
d_lr=0.00001
|
32 |
-
g2_lr=0.00001
|
33 |
-
d2_lr=0.00001
|
34 |
-
dropout=0.
|
35 |
-
dec_dropout=0.
|
36 |
-
n_critic=1
|
37 |
-
beta1=0.9
|
38 |
-
beta2=0.999
|
39 |
-
resume_iters=None
|
40 |
-
clipping_value=2
|
41 |
-
features=False
|
42 |
-
test_iters=10_000
|
43 |
-
num_test_epoch=30_000
|
44 |
-
inference_sample_num=1000
|
45 |
-
num_workers=1
|
46 |
-
mode="inference"
|
47 |
-
inference_iterations=100
|
48 |
-
inf_batch_size=1
|
49 |
-
protein_data_dir='data/akt'
|
50 |
-
drug_index='data/drug_smiles.index'
|
51 |
-
drug_data_dir='data/akt'
|
52 |
-
mol_data_dir='data'
|
53 |
-
log_dir='experiments/logs'
|
54 |
-
model_save_dir='experiments/models'
|
55 |
-
# inference_model=""
|
56 |
-
sample_dir='experiments/samples'
|
57 |
-
result_dir="experiments/tboard_output"
|
58 |
-
dataset_file="chembl45_train.pt"
|
59 |
-
drug_dataset_file="akt_train.pt"
|
60 |
-
raw_file='data/chembl_train.smi'
|
61 |
-
drug_raw_file="data/akt_train.smi"
|
62 |
-
inf_dataset_file="chembl45_test.pt"
|
63 |
-
inf_drug_dataset_file='akt_test.pt'
|
64 |
-
inf_raw_file='data/chembl_test.smi'
|
65 |
-
inf_drug_raw_file="data/akt_test.smi"
|
66 |
-
log_sample_step=1000
|
67 |
-
set_seed=True
|
68 |
-
seed=1
|
69 |
-
resume=False
|
70 |
-
resume_epoch=None
|
71 |
-
resume_iter=None
|
72 |
-
resume_directory=None
|
73 |
-
|
74 |
-
class ProtConfig(DrugGENConfig):
|
75 |
-
submodel="Prot"
|
76 |
-
inference_model="experiments/models/Prot"
|
77 |
-
|
78 |
-
class CrossLossConfig(DrugGENConfig):
|
79 |
-
submodel="CrossLoss"
|
80 |
-
inference_model="experiments/models/CrossLoss"
|
81 |
-
|
82 |
-
class NoTargetConfig(DrugGENConfig):
|
83 |
-
submodel="NoTarget"
|
84 |
-
inference_model="experiments/models/NoTarget"
|
85 |
-
|
86 |
-
|
87 |
-
model_configs = {
|
88 |
-
"Prot": ProtConfig(),
|
89 |
-
"CrossLoss": CrossLossConfig(),
|
90 |
-
"NoTarget": NoTargetConfig(),
|
91 |
-
}
|
92 |
-
|
93 |
-
|
94 |
-
with st.sidebar:
|
95 |
-
st.title("DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
|
96 |
-
st.write("[](https://arxiv.org/abs/2302.07868) [](https://github.com/HUBioDataLab/DrugGEN)")
|
97 |
-
|
98 |
-
with st.expander("Expand to display information about models"):
|
99 |
-
st.write("""
|
100 |
-
### Model Variations
|
101 |
-
- **DrugGEN-Prot**: composed of two GANs, incorporates protein features to the transformer decoder module of GAN2 (together with the de novo molecules generated by GAN1) to direct the target centric molecule design.
|
102 |
-
- **DrugGEN-CrossLoss**: composed of one GAN, the input of the GAN1 generator is the real molecules dataset and the GAN1 discriminator compares the generated molecules with the real inhibitors of the given target.
|
103 |
-
- **DrugGEN-NoTarget**: composed of one GAN, focuses on learning the chemical properties from the ChEMBL training dataset, no target-specific generation.
|
104 |
-
|
105 |
-
""")
|
106 |
-
|
107 |
-
with st.form("model_selection_from"):
|
108 |
-
model_name = st.radio(
|
109 |
-
'Select a model to make inference (DrugGEN-Prot and DrugGEN-CrossLoss models design molecules to target the AKT1 protein)',
|
110 |
-
('DrugGEN-Prot', 'DrugGEN-CrossLoss', 'DrugGEN-NoTarget')
|
111 |
-
)
|
112 |
-
|
113 |
-
model_name = model_name.replace("DrugGEN-", "")
|
114 |
-
|
115 |
-
molecule_num_input = st.number_input('Number of molecules to generate', min_value=1, max_value=100_000, value=1000, step=1)
|
116 |
-
|
117 |
-
seed_input = st.number_input("RNG seed value (can be used for reproducibility):", min_value=0, value=42, step=1)
|
118 |
-
|
119 |
-
submitted = st.form_submit_button("Start Computing")
|
120 |
-
|
121 |
-
|
122 |
-
|
123 |
-
if submitted:
|
124 |
-
# if submitted or ("submitted" in st.session_state):
|
125 |
-
# st.session_state["submitted"] = True
|
126 |
-
config = model_configs[model_name]
|
127 |
-
|
128 |
-
config.inference_sample_num = molecule_num_input
|
129 |
-
config.seed = seed_input
|
130 |
-
|
131 |
-
with st.spinner(f'Creating the trainer class instance for {model_name}...'):
|
132 |
-
trainer = Trainer(config)
|
133 |
-
with st.spinner(f'Running inference function of {model_name} (this may take a while) ...'):
|
134 |
-
results = trainer.inference()
|
135 |
-
st.success(f"Inference of {model_name} took {results['runtime']:.2f} seconds.")
|
136 |
-
|
137 |
-
with st.expander("Expand to see the generation performance scores"):
|
138 |
-
st.write("### Generation performance scores (novelty is calculated in comparison to the training dataset)")
|
139 |
-
st.success(f"Validity: {results['fraction_valid']}")
|
140 |
-
st.success(f"Uniqueness: {results['uniqueness']}")
|
141 |
-
st.success(f"Novelty: {results['novelty']}")
|
142 |
-
|
143 |
-
with open(f'experiments/inference/{model_name}/inference_drugs.txt') as f:
|
144 |
-
inference_drugs = f.read()
|
145 |
-
# st.download_button(label="Click to download generated molecules", data=inference_drugs, file_name=f'DrugGEN-{model_name}_denovo_mols.smi', mime="text/plain")
|
146 |
-
ste.download_button(label="Click to download generated molecules", data=inference_drugs, file_name=f'DrugGEN-{model_name}_denovo_mols.smi', mime="text/plain")
|
147 |
-
|
148 |
-
|
149 |
-
st.write("Structures of randomly selected 12 de novo molecules from the inference set:")
|
150 |
-
# from rdkit.Chem import Draw
|
151 |
-
# img = Draw.MolsToGridImage(mol_list, molsPerRow=5, subImgSize=(250, 250), maxMols=num_mols,
|
152 |
-
# legends=None, useSVG=True)
|
153 |
-
generated_molecule_list = inference_drugs.split("\n")
|
154 |
-
|
155 |
-
selected_molecules = random.choices(generated_molecule_list,k=12)
|
156 |
-
|
157 |
-
selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules]
|
158 |
-
# IPythonConsole.UninstallIPythonRenderer()
|
159 |
-
drawOptions = Draw.rdMolDraw2D.MolDrawOptions()
|
160 |
-
drawOptions.prepareMolsBeforeDrawing = False
|
161 |
-
drawOptions.bondLineWidth = 1.
|
162 |
-
|
163 |
-
molecule_image = Draw.MolsToGridImage(
|
164 |
-
selected_molecules,
|
165 |
-
molsPerRow=3,
|
166 |
-
subImgSize=(250, 250),
|
167 |
-
maxMols=len(selected_molecules),
|
168 |
-
# legends=None,
|
169 |
-
returnPNG=False,
|
170 |
-
# drawOptions=drawOptions,
|
171 |
-
highlightAtomLists=None,
|
172 |
-
highlightBondLists=None,
|
173 |
-
|
174 |
-
)
|
175 |
-
print(type(molecule_image))
|
176 |
-
# print(type(molecule_image._data_and_metadata()))
|
177 |
-
molecule_image.save("result_grid.png")
|
178 |
-
# png_data = io.BytesIO()
|
179 |
-
# molecule_image.save(png_data, format='PNG')
|
180 |
-
# png_data.seek(0)
|
181 |
-
|
182 |
-
# Step 2: Read the PNG image data as a PIL image
|
183 |
-
# pil_image = Image.open(png_data)
|
184 |
-
# st.image(pil_image)
|
185 |
-
st.image(molecule_image)
|
186 |
-
|
187 |
-
else:
|
188 |
-
st.warning("Please select a model to make inference")
|
189 |
-
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assets/DrugGEN_Figure1.gif
DELETED
Binary file (338 kB)
|
|
assets/DrugGEN_Figure1_1.gif
DELETED
Binary file (151 kB)
|
|
assets/DrugGEN_Figure1_2.gif
DELETED
Binary file (166 kB)
|
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assets/DrugGEN_G1_4.gif
DELETED
Binary file (386 kB)
|
|
assets/DrugGEN_G1_final2.gif
DELETED
Binary file (362 kB)
|
|
assets/DrugGEN_G2_3.gif
DELETED
Binary file (556 kB)
|
|
assets/DrugGEN_G2_final2.gif
DELETED
Binary file (588 kB)
|
|
assets/Selected_denovo_AKT1_inhibitors.png
DELETED
Binary file (507 kB)
|
|
assets/generator_1_mod.gif
DELETED
Binary file (472 kB)
|
|
assets/generator_2_mod.gif
DELETED
Binary file (622 kB)
|
|
assets/molecule_1.png
DELETED
Binary file (44.8 kB)
|
|
assets/molecule_2.png
DELETED
Binary file (53.5 kB)
|
|
data/akt/2x39_X39_BS_adj.csv
DELETED
The diff for this file is too large to render.
See raw diff
|
|
data/akt/2x39_X39_BS_adj_euc.csv
DELETED
The diff for this file is too large to render.
See raw diff
|
|
data/akt/2x39_X39_BS_annot.csv
DELETED
@@ -1,499 +0,0 @@
|
|
1 |
-
A,C,HD,N,NA,OA,SA
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1.0,0.0,0.0,0.0,0.0,0.0,0.0
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1.0,0.0,0.0,0.0,0.0,0.0,0.0
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1.0,0.0,0.0,0.0,0.0,0.0,0.0
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90 |
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91 |
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0.0,0.0,0.0,0.0,1.0,0.0,0.0
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92 |
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93 |
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98 |
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102 |
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1.0,0.0,0.0,0.0,0.0,0.0,0.0
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104 |
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105 |
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0.0,0.0,0.0,1.0,0.0,0.0,0.0
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106 |
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0.0,1.0,0.0,0.0,0.0,0.0,0.0
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107 |
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108 |
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109 |
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0.0,1.0,0.0,0.0,0.0,0.0,0.0
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110 |
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0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
111 |
-
0.0,0.0,0.0,1.0,0.0,0.0,0.0
|
112 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
113 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
114 |
-
0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
115 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
116 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
117 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
118 |
-
0.0,0.0,0.0,1.0,0.0,0.0,0.0
|
119 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
120 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
121 |
-
0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
122 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
123 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
124 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
125 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
126 |
-
0.0,0.0,0.0,1.0,0.0,0.0,0.0
|
127 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
128 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
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129 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
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130 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
131 |
-
0.0,0.0,0.0,1.0,0.0,0.0,0.0
|
132 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
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133 |
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0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
134 |
-
0.0,0.0,0.0,0.0,0.0,1.0,0.0
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135 |
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0.0,1.0,0.0,0.0,0.0,0.0,0.0
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136 |
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0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
137 |
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138 |
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0.0,0.0,1.0,0.0,0.0,0.0,0.0
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139 |
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0.0,0.0,1.0,0.0,0.0,0.0,0.0
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140 |
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153 |
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154 |
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155 |
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0.0,0.0,1.0,0.0,0.0,0.0,0.0
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data/akt/4gv1_0XZ_BS_adj.csv
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data/akt/4gv1_0XZ_BS_adj_euc.csv
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data/akt/4gv1_0XZ_BS_annot.csv
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data/akt/akt3_0XZ_BS_adj.csv
DELETED
The diff for this file is too large to render.
See raw diff
|
|
data/akt/akt3_0XZ_BS_adj_euc.csv
DELETED
The diff for this file is too large to render.
See raw diff
|
|
data/akt/akt3_0XZ_BS_annot.csv
DELETED
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464 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
465 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
466 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
467 |
-
0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
468 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
469 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
470 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
471 |
-
0.0,0.0,0.0,1.0,0.0,0.0,0.0
|
472 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
473 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
474 |
-
0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
475 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
476 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
477 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
478 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
479 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
480 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
481 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
482 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
483 |
-
0.0,0.0,0.0,1.0,0.0,0.0,0.0
|
484 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
485 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
486 |
-
0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
487 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
488 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
489 |
-
0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
490 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
491 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
492 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
493 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
494 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
495 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
496 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
497 |
-
1.0,0.0,0.0,0.0,0.0,0.0,0.0
|
498 |
-
0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
499 |
-
0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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data/akt_inhibitors.smi
DELETED
The diff for this file is too large to render.
See raw diff
|
|
data/akt_test.smi
DELETED
@@ -1,320 +0,0 @@
|
|
1 |
-
NC1(c2ccc(-c3nc4ccc(-c5ncc[nH]5)cn4c3-c3ccccc3)cc2)CCC1
|
2 |
-
Cc1cn2cc(-c3ccccc3)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc2n1
|
3 |
-
NC1(c2ccc(-c3nc4c(-c5ccc(F)cc5)cccn4c3-c3ccccc3)cc2)CCC1
|
4 |
-
NC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3c2CSC3)CC1
|
5 |
-
Cn1c(CC(=O)N2CCc3c2cccc3C(F)(F)F)nc(N2CCOCC2)cc1=O
|
6 |
-
CC1C(=O)Nc2ccc(NC(COc3cncc(-c4ccc5c(c4)C(C)C(=O)N5)c3)Cc3c[nH]c4ccccc34)cc21
|
7 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCc4n[nH]c(C)n4)cc3)nc2n1
|
8 |
-
NC1(c2ccc(-c3nn4c(-c5ccn[nH]5)cnc4cc3-c3ccccc3)cc2)CCC1
|
9 |
-
COC(=O)c1cnn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc12
|
10 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccccc4Cl)c3)cc12
|
11 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
12 |
-
Cc1n[nH]c2cnc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)cc12
|
13 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccccc1OCCN1CCCCC1
|
14 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(F)c4)cn3CCN3CCCC3)CC2)c1Br
|
15 |
-
NC1(c2ccc(-c3nc4c5cc(F)ccc5nn4c(NC4CC4)c3-c3ccccc3)cc2)CCC1
|
16 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1ccc2[nH]nc(C)c2c1
|
17 |
-
CCc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
18 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(C)N)cc21
|
19 |
-
NCC(NC(=O)c1cc(C2CC2)c(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
20 |
-
NCC(Cc1ccccc1C(F)(F)F)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
21 |
-
Cn1c(CC(=O)Nc2ccc(F)c(C(F)F)c2)nc(N2CCOCC2)cc1=O
|
22 |
-
CC(C)c1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn2n1
|
23 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)c3ccccc3)cc21
|
24 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4cccc(F)c4)c3)cc12
|
25 |
-
CCc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(Cl)cc4)C3)c12
|
26 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cc(F)c(F)c(F)c1
|
27 |
-
CCc1cnn2cc(-c3ccccc3)c(-c3ccc(CN4CC(c5n[nH]c(-c6cccc(C)n6)n5)C4)cc3)nc12
|
28 |
-
Cc1cc(-c2ccn[nH]2)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
29 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CC2CCN2)CC1
|
30 |
-
NC1(c2ccc(-c3nc4c(-c5ccc(F)cc5)cccn4c3-c3ccccc3)cc2)CCC1
|
31 |
-
Cc1ccc(F)cc1CC(N)COc1cncc(-c2ccc3[nH]nc(C)c3c2)c1
|
32 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccnc(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1-c1cnoc1
|
33 |
-
NC1(c2ccc(-c3nc4c(-c5cccc(F)c5)cccn4c3-c3ccccc3)cc2)CCC1
|
34 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCNCC2CC2)CC1
|
35 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cc(C(N)=O)ccc3-4)cc2)C1
|
36 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)C1CCC1
|
37 |
-
Nc1ncnc2nc(-c3ccc(CN4CCC(n5cnc6c(N)ncnc65)CC4)cc3)c(-c3ccccc3)cc12
|
38 |
-
Cc1cccc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c12
|
39 |
-
COC(=O)c1cccc2c1nn1cc(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc21
|
40 |
-
Cc1c[nH]c2ncnc(N3CC4(CCNCC4)c4ccccc43)c12
|
41 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCc4c[nH]cn4)cc3)nc2n1
|
42 |
-
Nc1cc(C=Cc2cncc(OCC(N)Cc3c[nH]c4ccccc34)c2)ccn1
|
43 |
-
CC1CN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)C(C)CN1
|
44 |
-
COCCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
45 |
-
NC1(C(=O)NC(CCO)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
46 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccc(C(F)(F)F)cc4)s3)cc12
|
47 |
-
COc1cc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)n2C)ccc1F
|
48 |
-
O=C(NC(c1ccc2ccccc2c1)C1CCNCC1)c1ccc2cnccc2c1
|
49 |
-
NC1(c2ccc(-c3nc4c5ccc(-c6ccc(O)nc6)cc5nn4cc3-c3ccccc3)cc2)CCC1
|
50 |
-
CNCCn1cc(-c2ccnc(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C2C=NOC2)CC1
|
51 |
-
NC1(c2ccc(-c3nc4ccc(-n5cccn5)cn4c3-c3ccccc3)cc2)CCC1
|
52 |
-
Cc1cc(C)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
53 |
-
NC1CCN(c2ncnc3[nH]cc(Cl)c23)C1
|
54 |
-
CC1COCCN1c1nc(N2CCOCC2C)c2ccc(-c3cccc(NS(=O)(=O)C(C)C)c3)nc2n1
|
55 |
-
Nc1nc(O)nc2nc(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)c(-c3ccccc3)cc12
|
56 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2ccc(Cl)c(C(F)(F)F)c2)sc1Cl
|
57 |
-
Cc1cc(-c2cn(CCNC(C)C)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)ccc1F
|
58 |
-
CNc1c(-c2ccccc2)c(-c2ccc(CN3CC(c4n[nH]c(-c5cccc(C)n5)n4)C3)cc2)nc2nc(C)nn12
|
59 |
-
CNCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
60 |
-
N#Cc1ccc(CC(N)C(=O)N2CCN(c3ncnc4ccccc34)CC2)cc1
|
61 |
-
Cc1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)c(C)n2n1
|
62 |
-
CCCC1OC2CC(=O)OC2C2=C1C(=O)c1c(O)cccc1C2=O
|
63 |
-
Cc1ccccc1-c1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2n1
|
64 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4cccc(Cl)c4)c3)cc12
|
65 |
-
COc1ccc(CC(N)C(=O)N2CCN(c3ncnc4ccccc34)CC2)cc1
|
66 |
-
CNC(=O)CC1CC(c2ccc(F)c(F)c2)C(NC(=O)c2cc(-c3c(Cl)cnn3C)c(Cl)o2)CN1
|
67 |
-
CCCC1NC(=O)C(CCCNC(=N)N)NC(=O)CN(C(=O)C(N)CCCNC(=N)N)CCCNC(=O)NCCCCCCN(CC(N)=O)C(=O)C(CCC(C)C)NC(=O)C(CN)NC(=O)C(Cc2ccc(O)cc2)NC1=O
|
68 |
-
CNc1ccc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c2c1
|
69 |
-
CCc1n[nH]c2ncnc(N3CCN(c4cc(Cl)cc(NCCN(C)C)c4C)CC3)c12
|
70 |
-
Cc1nc(N)nc2c1nc(-c1cc[nH]n1)c(=O)n2C1CCOCC1
|
71 |
-
Nc1ncccc1-c1nc2cccnc2n1-c1ccc(CC(=O)Nc2ccccc2)cc1
|
72 |
-
O=S(=O)(Nc1cc(-c2ccc3nccn3c2)cnc1Cl)c1ccc(F)cc1
|
73 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1c[nH]c2ccccc12
|
74 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCn3cncn3)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
75 |
-
NC(COc1cncc(-c2ccc3c(c2)C(c2ccccn2)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
76 |
-
O=C(N1CCN(c2ncnc3[nH]nc(Br)c23)CC1)C1(c2ccc(Br)cc2)CCNCC1
|
77 |
-
NC(COc1cncc(-c2ccc3[nH]nc(C4CC4)c3c2)c1)Cc1c[nH]c2ccccc12
|
78 |
-
CN(C)CCN1CCN(c2ccc3nc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
79 |
-
NC(COc1cncc(-c2ccc3cnc(F)cc3c2)c1)Cc1c[nH]c2ccccc12
|
80 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4cccc(F)c4)c3)cc2s1
|
81 |
-
CC1SCc2ncnc(N3CCN(C(=O)C(N)Cc4c[nH]c5ccccc45)CC3)c21
|
82 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCO)CC2c2ccc(F)c(F)c2)oc1Cl
|
83 |
-
N#Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
84 |
-
Fc1ccc(-c2cn3nc(C4CC4)nc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5cc(Cl)ccn5)n4)CC3)cc2)cc1
|
85 |
-
NCC(Cc1ccncc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
86 |
-
COC(=O)c1cc(Cl)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
87 |
-
COCCNC(=O)c1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
88 |
-
Nc1ncnc2c1cnn2C1CCN(Cc2ccc(-c3nc4ccnn4cc3-c3ccccc3)cc2)CC1
|
89 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCNCC3)c21.O=C(O)C(F)(F)F
|
90 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)cc1Br
|
91 |
-
CCN(CC)CCNC(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)nc2c1
|
92 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4ccc(C(F)(F)F)cc4)c3)cc12
|
93 |
-
COc1ccc(S(=O)(=O)Nc2cc(-c3ccc4nc(NC(C)=O)sc4c3)cnc2Cl)cc1
|
94 |
-
COC(=O)c1cn2cc(-c3ccccc3)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc2n1
|
95 |
-
CC(C)=C1C(=O)Nc2ccc(NC(COc3cncc(-c4ccc5c(c4)C(=C(C)C)C(=O)N5)c3)Cc3c[nH]c4ccccc34)cc21
|
96 |
-
NC(=O)c1cc(-c2ccn[nH]2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
97 |
-
Cc1ccc2c(c1)OCn1c-2nc(-c2ccc(C3(N)CC(O)(C4CC4)C3)cc2)c1-c1ccccc1
|
98 |
-
Nc1ncccc1-c1nc2cccnc2n1-c1ccc(CC(=O)Nc2ccccc2)cc1
|
99 |
-
CC(C)Nc1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn2n1
|
100 |
-
Oc1nc2ccc(NC(COc3cncc(-c4ccc5nc(O)sc5c4)c3)Cc3c[nH]c4ccccc34)cc2s1
|
101 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
102 |
-
COc1cc(Cl)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
103 |
-
c1ccc(-c2nnc[nH]2)c(Nc2ncnc3[nH]ccc23)c1
|
104 |
-
NC1(c2ccc(-c3nc4nc(Oc5ccccc5)ccn4c3-c3ccccc3)cc2)CCC1
|
105 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(c4ccc(Cl)cc4)C4COCCN4)CC3)c21
|
106 |
-
COc1ccc(S(=O)(=O)Nc2cncc(-c3ccc4nc(NC(C)=O)sc4c3)c2)cc1
|
107 |
-
COc1cc(COc2ccn3c(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc3n2)ccn1
|
108 |
-
NC(Cc1cc(F)cc(F)c1)C(=O)N1CCN(c2ncnc3ccccc23)CC1
|
109 |
-
Sc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
110 |
-
Cc1c(NCCN2CCCC2)cc(OCC(C)C)cc1N1CCN(c2ncnc3[nH]nc(Br)c23)CC1
|
111 |
-
NC1(c2ccc(-c3nc4c(C5CC5)cccn4c3-c3ccccc3)cc2)CCC1
|
112 |
-
NC(=O)c1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
113 |
-
CNC(=O)CC1CC(c2ccc(F)c(F)c2)C(NC(=O)c2cc(-c3c(Cl)cnn3C)c(Cl)o2)CN1
|
114 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccc(C(N)=O)cc3-4)cc2)C1
|
115 |
-
O=C1CC2OC(c3ccsc3)C3=C(C(=O)c4ccccc4C3=O)C2O1
|
116 |
-
N=C(c1ccccc1)n1c(=N)ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)cc21
|
117 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)sc1Cl
|
118 |
-
NC(COc1cncc(-c2ccc3[nH]ncc3c2)c1)Cc1c[nH]c2ccccc12
|
119 |
-
COC(=O)c1cn2cc(-c3ccccc3)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc2n1
|
120 |
-
COc1cc(-c2ncc[nH]2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
121 |
-
O=C(Nc1ccc2c(c1)CCO2)NC1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
122 |
-
Cn1c(CC(=O)N2CCc3c(F)cccc32)nc(N2CCOCC2)cc1=S
|
123 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccccc3-4)cc2)CCC1
|
124 |
-
NC(COc1cncc(-c2ccc3cnc(Cl)cc3c2)c1)Cc1c[nH]c2ccccc12
|
125 |
-
NC1CCN(c2ccnc3[nH]ccc23)CC1
|
126 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)c3ccccc3)cc21
|
127 |
-
NC(COc1cncc(-c2ccc3c(F)nccc3c2)c1)Cc1c[nH]c2ccccc12
|
128 |
-
Cl.Cn1ncc(Cl)c1-c1ccc(C(=O)NC2CNCCC2c2ccc(Cl)c(C(F)(F)F)c2)s1
|
129 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2cccc(F)c2)sc1Cl
|
130 |
-
NC1(c2ccc(-c3nc4c5ccc(-c6ccc(F)c(O)c6)cc5nn4cc3-c3ccccc3)cc2)CCC1
|
131 |
-
Cn1nccc1-c1ccc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)cc1
|
132 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)C(CC1CCCCC1)NC(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(N)=O
|
133 |
-
NC1(c2ccc(-c3nc4ncccn4c3-c3ccccc3)cc2)CCC1
|
134 |
-
CNCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
135 |
-
N#Cc1cccc(CC(N)COc2cncc(C=Cc3ccncc3)c2)c1
|
136 |
-
NC(COc1cncc(-c2ccc3[nH]nc(Cl)c3c2)c1)Cc1c[nH]c2ccccc12
|
137 |
-
Nc1ncccc1-c1nc2ccc(-c3ccccc3)nc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
138 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccncc3-4)cc2)CCC1
|
139 |
-
CN(C)CC1CN(C(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c2ccccc21
|
140 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)c(C2CC2)c1
|
141 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCCC3)CC2)c1-c1ccc(F)cc1
|
142 |
-
O=C(NC(c1ccc(Cl)c(Cl)c1)C1CNC1)c1ccc2cnccc2c1
|
143 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C)c3)cn2CCN(CC)C(C)C)CC1
|
144 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccccc4)C3)c12
|
145 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3ccc[nH]3)cc12
|
146 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4cccc(OC(F)(F)F)c4)c3)cc12
|
147 |
-
NC1(c2ccc(-c3nc4ccc(F)cn4c3-c3ccccc3)cc2)CCC1
|
148 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CNC)cc21
|
149 |
-
NC(COc1cnc2ccc(-c3ccncc3)cc2c1)Cc1c[nH]c2ccccc12
|
150 |
-
CC(C(=O)N1CCc2c(F)cccc21)c1nc(N2CCOCC2)cc(=O)[nH]1
|
151 |
-
Cn1nccc1-c1ccc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)cn1
|
152 |
-
Nc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc12
|
153 |
-
Cc1n[nH]c2ncc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)c(C#N)nc3-c3ccoc3)nc12
|
154 |
-
COc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
155 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(c5nnc(N)s5)CC4)cc3)nc2n1
|
156 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(-c7ccccn7)nn6c(NC(C)C)c5-c5ccccc5)cc4)C3)n[nH]2)n1
|
157 |
-
COc1cccc(CC2(N)CCN(c3ncnc4[nH]ccc34)CC2)c1
|
158 |
-
O=C(C(CNC1CCCCC1)c1ccc(Cl)cc1)N1CCN(c2ncnc3sc4c(c23)CCC4)CC1
|
159 |
-
Nc1ncnc2nc(-c3ccc(CN4CCC(c5nc6ccc(F)cc6[nH]5)CC4)cc3)c(-c3ccccc3)cc12
|
160 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CN4CCC(F)CC4)c4ccc(Cl)cc4)CC3)c21
|
161 |
-
CCc1cc2cc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)ccc2cn1
|
162 |
-
COc1ccc(C2(C(=O)N3CCN(c4ncnc5[nH]ccc45)CC3)CCNCC2)cc1
|
163 |
-
NC(CNc1ncc(-c2ccc3cnccc3c2)s1)Cc1ccc(C(F)(F)F)cc1
|
164 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2cccc(F)c2)sc1Cl
|
165 |
-
Cn1nccc1-c1csc(C(=O)NC2CNCCC2c2ccc(Cl)cc2)c1
|
166 |
-
Cc1ccc(-c2ccc3nn4cc(-c5ccccc5)c(-c5ccc(C6(N)CCC6)cc5)nc4c3c2)cc1
|
167 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)C1CCCN1C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CS)C(=O)NC(CCC(C)C)C(N)=O
|
168 |
-
NC1(c2ccc(-c3nc4cc(Cl)ccn4c3-c3ccccc3)cc2)CCC1
|
169 |
-
NC1(c2ccc(-c3ncc4cccn4c3-c3ccccc3)cc2)CCC1
|
170 |
-
Fc1ccc(-c2cn3c(Cl)cnc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
171 |
-
COC(=O)COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
172 |
-
CC(=O)Nc1ccc(-c2cncc(OCC(N)Cc3c[nH]c4ccccc34)c2)cc1
|
173 |
-
COc1c(F)ccc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c1F
|
174 |
-
NC1(C(=O)NCc2ccc(Cl)cc2)CCN(c2ccnc3[nH]ccc23)CC1
|
175 |
-
Nc1ncnc2nc(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)c(-c3ccccc3)cc12
|
176 |
-
NC1(c2ccc(-c3nc4c(-c5cn[nH]c5)cccn4c3-c3ccccc3)cc2)CCC1
|
177 |
-
c1ccc(-c2cc(-c3nn[nH]n3)cnc2-c2ccc(CNCc3ccc(-c4csnn4)cc3)cc2)cc1
|
178 |
-
NCC(NCc1ccc(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
179 |
-
CC(=O)Nc1nc2ccc(-c3ccnc(N(C)S(=O)(=O)c4ccccc4F)n3)cc2s1
|
180 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3cc[nH]c(=O)c3)cn2CCN2CCC2)CC1
|
181 |
-
NCC(Cc1cccc(F)c1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
182 |
-
O=C(N1CCN(c2ncnc3[nH]nc(Cl)c23)CC1)C1(c2ccc(Cl)c(Cl)c2)CCNCC1
|
183 |
-
Cc1cc(C(N)=O)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
184 |
-
O=C(NC(c1cccc(Cl)c1)C1CCNCC1)c1ccc2cnccc2c1
|
185 |
-
NC1(c2ccc(-c3nc4ccc(-c5cnc[nH]5)cn4c3-c3ccccc3)cc2)CCC1
|
186 |
-
NC1(c2ccc(-n3c(-c4ccccc4)nc4ccc(-c5ccccc5)nc43)cc2)CCC1
|
187 |
-
CC1(O)CC(O)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cc(C(=O)O)ccc3-4)cc2)C1
|
188 |
-
CCOCCN(CC(O)CN1CCCC2(CCN(c3ncnc(N)c3C3CC3)C2)C1)S(=O)(=O)c1c(C)cccc1C
|
189 |
-
NC1(Cc2cccc(OC(F)(F)F)c2)CCN(c2ncnc3[nH]ccc23)CC1
|
190 |
-
c1ccc(-c2cc3cccnc3nc2-c2ccc(CN3CCC(c4cc(-c5ccccn5)[nH]n4)CC3)cc2)cc1
|
191 |
-
CCCCCCCCCCCCCCCC(=O)OCC(COP(=O)(O)OC1C(O)C(OP(=O)(O)O)C(OP(=O)(O)O)C(OP(=O)(O)O)C1O)OC(=O)CCCCCCCCCCCCCCC
|
192 |
-
Cc1noc(C)c1S(=O)(=O)N(CCOC(C)C)CC(O)CN1CCCC2(CC(=O)c3cc(O)ccc3O2)C1
|
193 |
-
O=C(Cc1ccc(Cl)cc1)N1CCN(c2ncnc3[nH]cc(Br)c23)CC1
|
194 |
-
Cl.NCc1ccc(-n2c(-c3cccnc3N)nc3ccc(-c4ccccc4)nc32)cc1
|
195 |
-
CS(=O)(=O)c1ccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)cc1
|
196 |
-
Cc1c[nH]c2ncnc(N3CCC(NC(=O)c4ccccc4)C3)c12
|
197 |
-
CNC(=O)C1CCN(c2cnc(C(=O)Nc3csc(-c4nncn4C(C)C(F)(F)F)n3)cc2-n2cnc(C3CC3)c2)CC1
|
198 |
-
NCC(NC(=O)c1cc(-c2ccccc2)c(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
199 |
-
CC1Cc2c(Br)cccc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)n1C
|
200 |
-
Nc1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6cc[nH]c(=O)c6cc5-c5ccccc5)cc4)CC3)[nH]2)cn1
|
201 |
-
NC1(c2ccc(-c3nc4c5ccc(Br)cc5nn4cc3-c3ccccc3)cc2)CCC1
|
202 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(Cl)c3)cn2CCN2CCCC2)CC1
|
203 |
-
[C-]#[N+]c1cccc(C(=O)Nc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)c1
|
204 |
-
CCc1cnn(C)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)s1.Cl
|
205 |
-
Cc1cc(O)cc2c1NC(C)(CCCC(C)C)CC2
|
206 |
-
O=c1ccc(-c2cc(C3CCN(Cc4ccc(-c5nc6ncccc6cc5-c5ccccc5)cc4)CC3)n[nH]2)c[nH]1
|
207 |
-
O=S(=O)(NC1(c2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CCC1)c1cccc(F)c1
|
208 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccccc3-4)cc2)C1
|
209 |
-
Cn1c(CC(=O)N2CCc3ccc(F)cc32)nc(N2CCOCC2)cc1=O
|
210 |
-
N=C(c1ccccc1)n1c(=N)ccc2nc(-c3ccc(C4(N)CC(F)(F)C4)cc3)c(-c3ccccc3)cc21
|
211 |
-
NC1(c2ccc(-c3nc4c5cccc(-c6cn[nH]c6)c5nn4cc3-c3ccccc3)cc2)CCC1
|
212 |
-
N#Cc1cccc(-c2ccc3nn4cc(-c5ccccc5)c(-c5ccc(C6(N)CCC6)cc5)nc4c3c2)c1
|
213 |
-
CCn1c(-c2nonc2N)nc2c(-c3ccoc3)ncc(OCCCN)c21
|
214 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2-c2cnc(N)nc2)CC1
|
215 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccc(Cl)cc4)s3)cc12
|
216 |
-
NC1(c2ccc(-c3nc4ccc(-c5cn[nH]c5)cn4c3-c3ccccc3)cc2)CCC1
|
217 |
-
CC1OC2CC(=O)OC2C2=C1C(=O)c1ccccc1C2=O
|
218 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccsc3)COc3cccc(F)c3-4)cc2)C1
|
219 |
-
CC(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CC(=O)Nc5cccc(F)c5)cc4)c3n2)c1
|
220 |
-
CNC1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4
|
221 |
-
CCOC(=O)c1c(C)nc(NNC(=O)c2cccc3c(=O)c4ccccc4[nH]c23)nc1-c1ccc(OC)c(OC)c1
|
222 |
-
c1ccc(-c2cc3cnc(-n4ccnc4)nc3nc2-c2ccc(CN3CCC(c4nnc(-c5ccccn5)[nH]4)CC3)cc2)cc1
|
223 |
-
Cc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
224 |
-
CN(C)c1ccc(C(=O)NCc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)cc1
|
225 |
-
NCC(Cc1ccc(C(F)(F)F)cc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
226 |
-
CC1Cc2c(ccc(F)c2Cl)N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
227 |
-
C=Cc1ncc(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1ccc2cnccc2c1
|
228 |
-
Cc1cc(F)ccc1S(=O)(=O)NCC(O)CN1CCCC2(CCN(c3ncnc(N)c3C3CC3)C2)C1
|
229 |
-
NC1(C(=O)NC(CCO)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
230 |
-
NC(=O)COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
231 |
-
Cc1cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c(C)n1
|
232 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)cc3)cn2CCN2CC(F)C2)CC1
|
233 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)c(Cl)c(=O)[nH]1
|
234 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(Cl)cccc21
|
235 |
-
CCc1ncc(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1ccc2cnccc2c1
|
236 |
-
Cc1cc(-c2cccnc2)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
237 |
-
CNC1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4
|
238 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCCCCN)c21
|
239 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4cccc(C(F)(F)F)c4)c3)cc12
|
240 |
-
C=Cc1c(N)ncnc1N1CCC(c2nc(-c3cccc(F)c3)cn2CCN2CCCC2)CC1
|
241 |
-
Cc1cc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)ccc1F
|
242 |
-
Nc1ncccc1-c1nc2ccc(Nc3ccc(N4CCOCC4)cc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
243 |
-
CCc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(F)cc4F)C3)c12
|
244 |
-
NC1(c2ccc(-c3nc4c5cc(F)ccc5nn4cc3-c3ccccc3)cc2)CCC1
|
245 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc(Cl)c1
|
246 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNCC4CC4)c4ccc(Cl)cc4)CC3)c21
|
247 |
-
Nc1ncccc1-c1nc2ccc(-c3cccc(N4CCC(C(=O)N5CCOCC5)CC4)c3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
248 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOC(CF)C2)cc(=O)[nH]1
|
249 |
-
Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
250 |
-
NC1(c2ccc(-c3ncc4cnccc4c3-c3ccccc3)cc2)CCC1
|
251 |
-
COc1ncc(-c2cc3c(C)nc(N)nc3n(C3CCC(OCC(N)=O)CC3)c2=O)cn1
|
252 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCCNC)cc21
|
253 |
-
Cc1cccc(-c2nc(C3CCN(Cc4ccc(-c5nc6nccn6cc5-c5ccc(F)cc5)cc4)CC3)n[nH]2)n1
|
254 |
-
NC1(c2ccc(-c3nc4c5cc(-c6ccc(CO)cc6)ccc5nn4cc3-c3ccccc3)cc2)CCC1
|
255 |
-
NC1(c2ccc(-c3nc4ncc(-c5ccccc5)cn4c3-c3ccccc3)cc2)CCC1
|
256 |
-
CC1Cc2cc(F)c(F)cc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)n1C
|
257 |
-
NC1(c2ccc(-n3c(-c4ccccc4)nc4ccc(NCc5ccccc5)nc43)cc2)CCC1
|
258 |
-
OCCNC(c1ccc(Cl)cc1)c1ccc(-c2cn[nH]c2)cc1
|
259 |
-
Cc1cc(-c2cn(CCNCC(C)C)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)ccc1F
|
260 |
-
CCNc1nc(-c2ccoc2)c(-c2cnc3[nH]nc(C)c3n2)cc1OCC(N)Cc1ccccc1
|
261 |
-
Cn1c(CC(=O)N2CCc3c(F)cccc32)nc(N2CCOCC2)cc1=O
|
262 |
-
Cc1c(-c2ccn[nH]2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1C
|
263 |
-
NC(COc1cncc(-c2ccc3[nH]nc(-c4ccc[nH]4)c3c2)c1)Cc1c[nH]c2ccccc12
|
264 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccc(F)c(F)c4)s3)cc12
|
265 |
-
O=S(=O)(NCCNCC=Cc1ccc(Br)cc1)c1cccc2cnccc12
|
266 |
-
COC(=O)c1c(C)nc(NNC(=O)c2cccc3c(=O)c4ccccc4[nH]c23)nc1-c1ccc(OC)cc1
|
267 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OC(CN)c3ccccc3)cc21
|
268 |
-
CC(C)NCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
269 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccc(F)cc4)c3)cc2[nH]1
|
270 |
-
Nc1ncnc(N2CCC(c3nc(-c4cccc(F)c4)cn3CCN3CCCC3)CC2)c1Br
|
271 |
-
O=C(NC(c1ccc(Cl)cc1)C1CCNCC1)c1ccc2cnccc2c1
|
272 |
-
Cc1cc(-c2cn(CCN(C)C)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
273 |
-
CNc1nccc(-c2ccc(C(=O)NCC(C)c3ccc(Cl)cc3Cl)s2)n1
|
274 |
-
CC1Cc2cc(F)ccc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)[nH]1
|
275 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
276 |
-
COc1ccccc1C(=O)N1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
277 |
-
Nc1cc2cc(-c3cnc(NCC(N)Cc4ccc(C(F)(F)F)cc4)s3)ccc2cn1
|
278 |
-
CSc1nc2nc(-c3ccc(CN4CCC(c5n[nH]c(-c6cccc(C)n6)n5)CC4)cc3)c(-c3ccccc3)cn2n1
|
279 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccnc(C(F)(F)F)c4)cn3CCNC3CC3)CC2)c1Cl
|
280 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
281 |
-
COc1ccc(COc2ccn3c(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc3n2)cn1
|
282 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)nc12
|
283 |
-
Cc1cnn(C)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)s1.Cl
|
284 |
-
N=C(c1ccccc1)n1c(=N)ccc2nc(-c3ccc(C4(N)CC(F)(F)C4)cc3)c(-c3ccccc3)cc21
|
285 |
-
CC(C)Nc1c(-c2ccccc2)c(-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)nc2nc(-c3ccccn3)nn12
|
286 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCc4nccs4)cc3)nc2n1
|
287 |
-
NC1(c2ccc(-n3c(-c4cccc(Cl)c4)nc4ccc(-c5cccc(N6CCOCC6)c5)nc43)cc2)CCC1
|
288 |
-
NC1(C(=O)NC(CCN2CCCCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
289 |
-
NC1(c2ccc(-c3nc4c(F)cccn4c3-c3ccccc3)cc2)CCC1
|
290 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4csc5ccccc45)c3)cc12
|
291 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(Cl)c3)cn2CCN2CCC2)CC1
|
292 |
-
Cc1cc(-c2cn(CCNC3CC3)c(C3CCN(c4ncnc(N)c4-c4cn[nH]c4)CC3)n2)ccc1F
|
293 |
-
NC(=O)c1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
294 |
-
Cc1cc(-c2cn(CC3CNC3)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
295 |
-
NC1(c2ccc(-c3nc4c(-c5ccn[nH]5)cccn4c3-c3ccccc3)cc2)CCC1
|
296 |
-
Cc1cc(-c2cn(CCNCC(C)C)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
297 |
-
Cc1c[nH]c2ncnc(Nc3ccccc3-c3nnc[nH]3)c12
|
298 |
-
CCn1c(-c2nonc2N)nc2c(C#CCCO)ncc(OCCCN)c21
|
299 |
-
CC(C)NCC(Cc1ccc(Cl)c(F)c1)C(=O)N1CCN(c2ncnc3c2C(C)OC3)CC1
|
300 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(F)cccc21
|
301 |
-
CN1CC(C(NC(=O)c2ccc3cnccc3c2)c2ccc(Cl)c(Cl)c2)C1
|
302 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CN(CCCCCCN)C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(=O)N(CCN)CC(N)=O
|
303 |
-
Cc1c(NCCN2CCCC2)cc(C(=O)CCC(F)(F)F)cc1N1CCN(c2ncnc3[nH]nc(Br)c23)CC1
|
304 |
-
NCC(Cc1cccc(C(F)(F)F)c1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
305 |
-
CSc1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6cccc(C)n6)n5)C4)cc3)c(-c3ccccc3)cn2n1
|
306 |
-
NC(COc1cncc(-c2ccc3c(c2)C(c2cccs2)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
307 |
-
Cn1nnnc1-c1cnc(-c2ccc(CN3CCC(n4c(=O)[nH]c5ccccc54)CC3)cc2)c(-c2ccccc2)c1
|
308 |
-
CC(C)c1cccc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c1
|
309 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNCC4CC4)c4ccc(C(F)(F)F)c(F)c4)CC3)c21
|
310 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(Cl)cc4)C3)c12
|
311 |
-
NC1(c2ccc(-n3c(-c4ccccc4O)nc4ccc(-c5ccccc5)nc43)cc2)CCC1
|
312 |
-
CC(C)(Cc1ccccc1)C1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
313 |
-
Cc1ccc(CC(N)C(=O)N2CCN(c3ncnc4ccccc34)CC2)cc1
|
314 |
-
Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(CC(=O)Nc2ccccc2)cc1
|
315 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(n5ncc6c(N)ncnc65)CC4)cc3)nc2n1
|
316 |
-
NC1(c2ccc(-c3nn4c(-c5ccn[nH]5)cnc4cc3-c3ccccc3)cc2)CCC1
|
317 |
-
Cl.NCc1ccc(-n2c(-c3cccnc3N)nc3ccc(-c4cn[nH]c4)nc32)cc1
|
318 |
-
COC1(C)CN(c2cnc(C(=O)Nc3csc(-c4nncn4C4CC4)n3)cc2-n2cnc(C3CC3)c2)C1
|
319 |
-
N#Cc1ncc(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1ccc2cnccc2c1
|
320 |
-
NC(COc1cncc(-c2ccc3c(c2)C(c2ccccc2)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
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data/akt_train.smi
DELETED
The diff for this file is too large to render.
See raw diff
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data/chembl_test.smi
DELETED
The diff for this file is too large to render.
See raw diff
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data/dataset_download.sh
DELETED
@@ -1,6 +0,0 @@
|
|
1 |
-
#!/bin/sh
|
2 |
-
pip install gdown
|
3 |
-
|
4 |
-
gdown --fuzzy "https://drive.google.com/file/d/1kDpTm36X3ugpr6Ooo4Fg_dkNRZhQ5EMC/view?usp=share_link"
|
5 |
-
|
6 |
-
gdown --fuzzy "https://drive.google.com/file/d/13h465yaIbrAp5tcGbIwhxriorejr6Fsz/view?usp=share_link"
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data/decoders/__init__.txt
DELETED
File without changes
|
data/encoders/__init__.txt
DELETED
File without changes
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data/filtered_akt_inhibitors.smi
DELETED
@@ -1,1600 +0,0 @@
|
|
1 |
-
Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCCC2)CC1
|
2 |
-
COC(=O)c1ccc(-c2c(N)ncnc2N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCC3)CC2)cc1
|
3 |
-
Cl.Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
4 |
-
NCC(NC(=O)c1ccc(-c2c[nH]c3ncccc23)s1)c1ccccc1
|
5 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccnc(OC)c3)cn2CCN2CCC2)CC1
|
6 |
-
NC1(c2ccc(-c3nc4cc(Cl)c(Cl)cn4c3-c3ccccc3)cc2)CCC1
|
7 |
-
c1ccc(-c2cn3ccnc3nc2-c2ccc(CN3CCC(c4nc5cccnc5[nH]4)CC3)cc2)cc1
|
8 |
-
NC1CCCN(c2ncnc3[nH]cc(Cl)c23)C1
|
9 |
-
CNCCn1cc(-c2ccnc(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C2C=NOC2)CC1
|
10 |
-
CNCCn1cc(-c2ccc(F)c(C)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
11 |
-
COc1cccc2c1-c1nc(-c3ccc(C4(N)CC(O)(C5CC5)C4)cc3)c(-c3ccccc3)n1CO2
|
12 |
-
NC1(C(=O)N2CCc3ccccc3C2)CCN(c2ncnc3[nH]ccc23)CC1
|
13 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccc(C(F)(F)F)cc4)s3)cc12
|
14 |
-
Nc1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6nc(N7CCN(CCO)CC7)ncc6cc5-c5ccccc5)cc4)CC3)[nH]2)cn1
|
15 |
-
Cc1ccc(-c2nc3c(C)nc(N)nc3n(C3CCC(O)CC3)c2=O)cn1
|
16 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3nccnc3-4)cc2)CC(O)(C2CC2)C1
|
17 |
-
Cn1cc(-c2cnc3c(-c4csc(C(=O)NC5CCCCC5N)c4)cnn3c2)cn1
|
18 |
-
CC1Cc2c(F)cccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)n1C
|
19 |
-
CC(C)(C)NCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
20 |
-
C=Cc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
21 |
-
NC(=O)c1ccc(NC2CNCCC2c2ccc(F)c(Cl)c2)c2cncnc12
|
22 |
-
NC1(c2ccc(-c3nc4nc(O)ccn4c3-c3ccccc3)cc2)CCC1
|
23 |
-
COC(=O)c1cnn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CCC(c5n[nH]c(-c6ccccn6)n5)CC4)cc3)nc12
|
24 |
-
c1ccc(-c2cn3ccnc3nc2-c2ccc(CN3CCC(c4cnc5ccccc5n4)CC3)cc2)cc1
|
25 |
-
Cc1c[nH]c2ncnc(Nc3ccccc3-c3nnc[nH]3)c12
|
26 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccccc3-4)cc2)CCC1
|
27 |
-
Cc1c(NCCN2CCCC2)cc(CCC(C)(C)C)cc1N1CCN(c2ncnc3[nH]nc(Br)c23)CC1
|
28 |
-
N#Cc1ccc2nc(C3CCN(Cc4ccc(-c5nc6ccnn6cc5-c5ccccc5)cc4)CC3)[nH]c2c1
|
29 |
-
NC1(C(=O)NC(Cc2ccccc2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
30 |
-
NC(=O)Nc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
31 |
-
NC(=O)Nc1ccc2c(c1)C(=Cc1cc(-c3cccc(F)c3)c[nH]1)C(=O)N2
|
32 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3F)CC1)c1ccc(Cl)cc1
|
33 |
-
CS(=O)(=O)c1ccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)cc1
|
34 |
-
CC(=O)Nc1nc2ccc(-c3ccnc(N(C)S(=O)(=O)c4ccc(C)cc4)n3)cc2s1
|
35 |
-
CCOCCN(CC(O)CN1CCCC2(CC(=O)c3cc(O)ccc3O2)C1)S(=O)(=O)c1c(C)noc1C
|
36 |
-
COCCNCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
37 |
-
Cc1cn2c(NC(C)C)c(-c3ccccc3)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc2n1
|
38 |
-
COc1nn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CCC(c5n[nH]c(-c6ccccn6)n5)CC4)cc3)nc2c1CO
|
39 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(C)nn6cc5-c5ccc(F)cc5F)cc4)C3)n[nH]2)n1
|
40 |
-
CC(C)NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
41 |
-
Cc1cccc(C)c1S(=O)(=O)N1CCCC1C(O)CN1CCCC2(CCN(c3ncnc(N)c3C3CC3)C2)C1
|
42 |
-
Oc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
43 |
-
Cc1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4OC(C)C)CC3)n2)ccc1F
|
44 |
-
CCc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(F)cc4F)C3)c12
|
45 |
-
Cc1cc(C)c(CC(N)COc2cncc(-c3ccc4[nH]nc(C)c4c3)c2)c(C)c1
|
46 |
-
Nc1nccc(-c2ccc(C(=O)NCCc3ccc(Cl)cc3Cl)s2)n1
|
47 |
-
NC(=O)Nc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
48 |
-
CC1Cc2c(Cl)cccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
49 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cccnc3-4)cc2)C1
|
50 |
-
CCOC(=O)c1c(C)nc(NNC(=O)c2cccc3c(=O)c4ccccc4[nH]c23)nc1-c1ccc(F)cc1
|
51 |
-
Cc1nc2ccccc2n1C1CCN(Cc2ccc(-c3nc4ncnc(N)c4cc3-c3ccccc3)cc2)CC1
|
52 |
-
NC1(C(=O)NCc2ccc(Cl)cc2)CCN(c2ncnc3[nH]c(=O)[nH]c23)CC1
|
53 |
-
Cc1nc2nc(-c3ccc(CN4CCC(c5n[nH]c(-c6ccccn6)n5)CC4)cc3)c(-c3cccc(F)c3)cn2n1
|
54 |
-
CC(C)(C)c1ccc(CC2(N)CCN(c3ncnc4[nH]ccc34)CC2)cc1
|
55 |
-
O=C(N1CCN(c2ncnc3[nH]nc(Br)c23)CC1)C1(c2ccc(Cl)cc2)CCNCC1
|
56 |
-
NC1(COCc2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
57 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNC4CCOCC4)c4ccc(Cl)c(F)c4)CC3)c21
|
58 |
-
CN(C)C(=O)N1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
59 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(c5nc(-c6cccnc6)no5)CC4)cc3)nc2n1
|
60 |
-
CC1Cc2c(cccc2C(F)(F)F)N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
61 |
-
Nc1ncccc1-c1nc2ccc(-c3ccccc3)nc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
62 |
-
Cc1cc(-c2cn(CCNC(C)C)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
63 |
-
NC1(c2ccc(-c3nc4c5cccc(Br)c5nn4cc3-c3ccccc3)cc2)CCC1
|
64 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(O)nc5)cnc4cc3-c3ccccc3)cc2)CCC1
|
65 |
-
NC(COc1cncc(-c2ccc3c(c2)C(c2c[nH]c4ccccc24)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
66 |
-
c1nc(N2CCc3[nH]cnc3C2)c2cc[nH]c2n1
|
67 |
-
NCC(Cc1ccc(C(F)(F)F)cc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
68 |
-
NC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ccnc3ccccc23)CC1
|
69 |
-
CCNc1nc(-c2ccoc2)c(-c2cnc3[nH]nc(C)c3n2)cc1OCC(N)Cc1ccccc1
|
70 |
-
Cc1[nH]c(C=C2C(=O)Nc3ccc(NC(N)=O)cc32)c(C)c1CCC(=O)O
|
71 |
-
NC(COc1cncc(-c2ccc3c(c2)CC(=O)N3)c1)Cc1c[nH]c2ccccc12
|
72 |
-
CC(C)(Cc1cccs1)C1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
73 |
-
Cn1ncc(Cl)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)cc1
|
74 |
-
CC(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CC(=O)Nc5cccc(F)c5)cc4)c3n2)c1
|
75 |
-
Clc1ccc(C2(c3ccc(-c4ncnc5[nH]cnc45)cc3)CCNCC2)cc1
|
76 |
-
NCC(Cc1cccc(F)c1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
77 |
-
NC(COc1cncc(C=Cc2ccncc2)c1)Cc1cccc2ccccc12
|
78 |
-
CC1SCc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c21
|
79 |
-
CCOc1cccc2c1-c1nc(-c3ccc(C4(N)CC(O)(C5CC5)C4)cc3)c(-c3ccccc3)n1CO2
|
80 |
-
N#Cc1ncc(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1ccc2cnccc2c1
|
81 |
-
NC1(c2ccc(-c3nn4cccc4cc3-c3ccccc3)cc2)CCC1
|
82 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc2ocnc12
|
83 |
-
N=C(c1ccccc1)n1c(=N)ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)cc21
|
84 |
-
NC1(c2ccc(-c3nc4ccc(C(=O)O)cn4c3-c3ccccc3)cc2)CCC1
|
85 |
-
CCOCCN(CC(O)CN1CCCC2(CCc3cc(C#N)ccc3O2)C1)S(=O)(=O)c1c(C)cccc1C
|
86 |
-
COC(=O)c1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
87 |
-
Cc1c(NCCN2CCCC2)cc(OCC(C)C)cc1N1CCN(c2ncnc3[nH]nc(Br)c23)CC1
|
88 |
-
Cl.Cn1ncc(Br)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)s1
|
89 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CCC(C(F)(F)F)CC2)cn1
|
90 |
-
CC(C)Oc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CC2CNC2)CC1
|
91 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)oc1Cl.O=C(O)C(O)C(O)C(=O)O
|
92 |
-
Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1Cl
|
93 |
-
Cn1cc(-c2cnc3c(-c4csc(C(=O)NC5CCCCC5N)c4)cnn3c2)cn1
|
94 |
-
CNC(=O)CC1CC(c2ccc(F)c(F)c2)C(NC(=O)c2cc(-c3c(Cl)cnn3C)c(Cl)o2)CN1
|
95 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccccc4)C3)c12
|
96 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(C(F)(F)F)c(F)c4)c3)cc12
|
97 |
-
COc1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
98 |
-
Cc1cc(-c2cn(CCNC3CCCC3)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)ccc1F
|
99 |
-
CC(=O)NCCC1CC(c2ccc(F)c(F)c2)C(NC(=O)c2cc(-c3c(Cl)cnn3C)c(Cl)o2)CN1
|
100 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccncc3-4)cc2)C1
|
101 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3cccc(F)c3)cn2CCN2CCCC2)CC1
|
102 |
-
Cc1ccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)cc1
|
103 |
-
NC1(c2ccc(-c3nc4cc(C(=O)O)ccn4c3-c3ccccc3)cc2)CCC1
|
104 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc12
|
105 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)cc1OCC1CCNCC1
|
106 |
-
Cl.Cn1ncc(CO)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)s1
|
107 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(C(F)(F)F)cc3)cn2CCN2CCCC2)CC1
|
108 |
-
CCN(CC)CCn1cc(-c2ccc(F)c(C)c2)nc1C1CCN(c2ncnc(N)c2OC(C)C)CC1
|
109 |
-
COC1(C)CCN(c2cnc(C(=O)Nc3csc(-c4nncn4C4CC4)n3)cc2-n2cnc(C3CC3)c2)CC1
|
110 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCCCNCCc3ccc(OC)cc3)c21
|
111 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CN(CCCN)C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(=O)N(CCCN)CC(N)=O
|
112 |
-
N#Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
113 |
-
N#Cc1cc(Cl)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
114 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)c3ccccc3)cc21
|
115 |
-
CN(C)CCN1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccc(=O)[nH]c7)[nH]6)CC5)cc4)nc3n2)CC1
|
116 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4cccc(F)c4)s3)cc12
|
117 |
-
O=C(NC(c1ccccc1)C1CCNCC1)c1ccc2cnccc2c1
|
118 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccccc1OCc1cccnc1
|
119 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(n5ncc6c(N)ncnc65)CC4)cc3)nc2n1
|
120 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(F)cc4)C3)c12
|
121 |
-
CC1CN(c2cc(=O)[nH]c(CC(=O)N3CCc4c(F)cccc43)n2)CCO1
|
122 |
-
CSc1nc2nc(-c3ccc(CN4CCC(c5n[nH]c(-c6ncccn6)n5)CC4)cc3)c(-c3ccccc3)cn2n1
|
123 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CC2CCN2)CC1
|
124 |
-
NC(COc1cncc(-c2ccc3c(c2)CC(=O)N3)c1)Cc1ccccc1
|
125 |
-
NC1(Cc2ccc3ccccc3c2)CCN(c2ncnc3[nH]ccc23)CC1
|
126 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccnc(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1Cl
|
127 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CC(=O)NC3CC3)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
128 |
-
O=c1[nH]c(-c2ccccc2)cn1C1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
129 |
-
CC(C)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCC2)CC1
|
130 |
-
CN1CC(Cn2cc(-c3ccc(F)c(C(F)(F)F)c3)nc2C2CCN(c3ncnc(N)c3C(N)=O)CC2)C1
|
131 |
-
Cn1cc(C(CN)c2cncc(C=Cc3ccncc3)c2)c2ccccc21
|
132 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccnc(C(F)(F)F)c4)cn3CCNCCCCl)CC2)c1Br
|
133 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccsc3)cn2CCN2CCC2)CC1
|
134 |
-
CC1CN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC(C)O1
|
135 |
-
CCC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccncc3-4)cc2)C1
|
136 |
-
NC(Cc1ccc(C(F)(F)F)cc1)C(=O)N1CCN(c2ncnc3ccccc23)CC1
|
137 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2(c3ccc(Cl)c(Cl)c3)CCNCC2)oc1Cl
|
138 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNC(C)(C)CO)c4ccc(Cl)cc4)CC3)c21
|
139 |
-
Cn1c(CC(=O)Nc2cccc(C3CC3)c2)nc(N2CCOCC2)cc1=O
|
140 |
-
Nc1ncccc1-c1nc2cc(-c3cccnc3)cnc2n1-c1ccc(CNC(=O)c2cccc(F)c2)cc1
|
141 |
-
NC1(C(=O)NC(c2ccccc2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
142 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)n1C
|
143 |
-
Cc1cc(O)cc2c1OC(C)(CCCC(C)C)CC2
|
144 |
-
CC(C)CNC(c1ccc(Cl)cc1)c1ccc(-c2cn[nH]c2)cc1
|
145 |
-
COc1ccc(COc2ccn3c(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc3n2)cn1
|
146 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4c(F)cc(F)cc4F)c3)cc12
|
147 |
-
CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1
|
148 |
-
NC1(c2ccc(-c3nc4ccc(Cl)cn4c3-c3ccccc3)cc2)CCC1
|
149 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CCN(CC(F)(F)F)CC2)cn1
|
150 |
-
CN1CCC2(CC1)CN(c1ncnc3[nH]ccc13)c1ccccc12
|
151 |
-
NC1(CNC(=O)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]cc(Cl)c23)C1
|
152 |
-
CCC(C)(C)NCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
153 |
-
Fc1ccc(-c2cn3ccnc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
154 |
-
NCC1(c2ccc(Cl)cc2)CCN(c2ccnc3[nH]ccc23)CC1
|
155 |
-
CC(C)Nc1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn2n1
|
156 |
-
CC1CN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CCN1
|
157 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CN(CCCN)C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(=O)N(CCCCN)CC(N)=O
|
158 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(c4ccc(Cl)cc4)C4CCCCN4)CC3)c21
|
159 |
-
Cc1c(OCC(N)Cc2c[nH]c3ccccc23)cncc1-c1ccc2cnccc2c1
|
160 |
-
O=C(Nc1cccc(C(F)(F)F)c1)NC1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
161 |
-
NC1(c2ccc(-c3nn4c(Br)cnc4cc3-c3ccccc3)cc2)CCC1
|
162 |
-
NC1(c2ccc(-c3nc4c5cc(-c6ccc(CO)cc6)ccc5nn4cc3-c3ccccc3)cc2)CCC1
|
163 |
-
Cc1nc2nc(-c3ccc(CN4CCC(n5ncc6c(N)ncnc65)CC4)cc3)c(-c3ccccc3)cn2n1
|
164 |
-
Cc1cc2cc(-c3nnc(NCC(N)Cc4ccc(C(F)(F)F)cc4)s3)ccc2cn1
|
165 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c(Cl)[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2n1
|
166 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c1ccc(F)c2F
|
167 |
-
OCCN1CCN(c2ccc3nc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
168 |
-
Cc1cc(-c2cn(CCNCC3CC3)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
169 |
-
CSc1nc2nc(-c3ccc(CN4CCC(c5n[nH]c(-c6cnccn6)n5)CC4)cc3)c(-c3ccccc3)cn2n1
|
170 |
-
CC(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CC(=O)Nc5ccccc5)cc4)c3n2)c1
|
171 |
-
O=C(Nc1cccc(F)c1)NC1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
172 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1c[nH]c2ccccc12
|
173 |
-
Cc1cc(-c2ccc3nn4cc(-c5ccccc5)c(-c5ccc(C6(N)CCC6)cc5)nc4c3c2)[nH]n1
|
174 |
-
CC(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CN)cc4)c3n2)c1.Cl
|
175 |
-
CCn1c(-c2nonc2N)nc2c(-c3ccccc3)ncc(OCCCN)c21
|
176 |
-
Clc1ccc(C(NC2CC2)c2ccc(-c3cn[nH]c3)cc2)cc1
|
177 |
-
COc1ccc(COc2ccn3c(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc3n2)cn1
|
178 |
-
Cc1cc(-c2cccnc2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
179 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
180 |
-
Nc1cc(N2CCC(c3nc(-c4ccc(F)c(F)c4)cn3CCN3CCCC3)CC2)ncn1
|
181 |
-
CNC1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4
|
182 |
-
COC(=O)c1cccc2c1nn1cc(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc21
|
183 |
-
CN(C)C1CCN(C(=O)c2c[nH]c(C=C3C(=O)Nc4ccc(NC(N)=O)cc43)c2)C1
|
184 |
-
CN1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ncccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
185 |
-
COC(=O)c1ccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)cn1
|
186 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3cnoc3)cn2CCN2CCC2)CC1
|
187 |
-
NC1(Cc2cccc3ccccc23)CCN(c2ncnc3[nH]ccc23)CC1
|
188 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc2c1NCCC2
|
189 |
-
CCCOc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2n1
|
190 |
-
CCNC(=O)c1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
191 |
-
CCOC(=O)c1cncc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)c1
|
192 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccc(F)cc1
|
193 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)n1C
|
194 |
-
CC(C)C(C(=O)Nc1ccc(F)cc1)c1nc(N2CCOCC2)cc(=O)[nH]1
|
195 |
-
Cn1c(CC(=O)N2CC(C)(C)c3c(Cl)cccc32)nc(N2CCOCC2)cc1=O
|
196 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCC3)CC2)c1-c1ccccc1F
|
197 |
-
Cc1cc(-c2cn(CCNC(C)(C)C)c(C3CCN(c4ncnc(N)c4-c4cn[nH]c4)CC3)n2)ccc1F
|
198 |
-
Cc1cc(C)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
199 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4cccc(OCCN5CCOCC5)c4)c3)cc12
|
200 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CC(F)(F)C2)cn1
|
201 |
-
CCc1cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c(OC)n1
|
202 |
-
CNC(=O)Nc1ccc(CNc2c(C(=O)Nc3ccc(SC(F)(F)F)cc3)cnn2C)cn1
|
203 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1ccc2[nH]nc(C)c2c1
|
204 |
-
COC(=O)c1cnn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CC(c5n[nH]c(-c6cccc(C)n6)n5)C4)cc3)nc12
|
205 |
-
Cc1c[nH]c2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c12
|
206 |
-
NC(COc1cncc(-c2ccc3c(c2)C(=Cc2ccc[nH]2)C(=O)N3)c1)Cc1ccccc1
|
207 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1cccnc1
|
208 |
-
CCn1c(-c2nonc2N)nc2cncc(CNC3CCNCC3)c21
|
209 |
-
NC1(c2ccc(-c3nc4c(F)cccn4c3-c3ccccc3)cc2)CCC1
|
210 |
-
NC1(c2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CCCCC1
|
211 |
-
COc1ncc(-c2cc3c(C)nc(N)nc3n(C3CCC(OCO)CC3)c2=O)cc1F
|
212 |
-
NC1(Cc2c(Cl)cccc2Cl)CCN(c2ncnc3[nH]ccc23)CC1
|
213 |
-
NC(COc1cncc(C=Cc2ccncc2)c1)Cc1ccc2ccccc2c1
|
214 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(OC(F)F)cccc21
|
215 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccc(C(F)(F)F)cc1
|
216 |
-
O=c1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6nc(N7CCN(CCO)CC7)ncc6cc5-c5ccccc5)cc4)CC3)[nH]2)c[nH]1
|
217 |
-
Cc1cc(-c2cn(CCN(C)C)c(C3CCN(c4ncnc(N)c4-c4cn[nH]c4)CC3)n2)ccc1F
|
218 |
-
COC1CCN(c2cnc(C(=O)Nc3csc(-c4nncn4C(C)C(F)(F)F)n3)cc2-n2cnc(C3CC3)c2)C1
|
219 |
-
Cc1c(O)cc2c(c1C)OC(C)(CCCC(C)C)CC2
|
220 |
-
CC(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CC(=O)Nc5ccccc5)cc4)c3n2)c1
|
221 |
-
CCN(CCn1cc(-c2ccc(F)c(C)c2)nc1C1CCN(c2ncnc(N)c2OC(C)C)CC1)C(C)C
|
222 |
-
COc1cc(C(N)=O)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
223 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccsc3)COc3cccc(F)c3-4)cc2)CC(O)(C2CC2)C1
|
224 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccc(C(N)=O)cc3-4)cc2)C1
|
225 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4c[nH]c5cccnc45)cnc3-c3ccoc3)cc12
|
226 |
-
O=C(Cc1nc(N2CCOC(CF)C2)cc(=O)[nH]1)N1CCc2c(F)cccc21
|
227 |
-
CCONC(=O)c1ccc2c(c1)OCn1c-2nc(-c2ccc(C3(N)CC(C)(O)C3)cc2)c1-c1ccccc1
|
228 |
-
O=c1[nH]c2ccccc2n1C1CCN(Cc2ccc(-c3nc4cc5[nH]cnc5cc4nc3-c3ccccc3)cc2)CC1
|
229 |
-
NC(CNc1nnc(-c2ccc3[nH]ncc3c2)s1)Cc1ccc(Cl)cc1Cl
|
230 |
-
CC(C)Oc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN(C)C)CC1
|
231 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc(Br)c1
|
232 |
-
COC(=O)COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
233 |
-
COc1nn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc2c1CO
|
234 |
-
NC1CCN(c2ncnc3[nH]ncc23)CC1
|
235 |
-
CC(C)(Cc1ncc[nH]1)C1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
236 |
-
NC(=O)Nc1ccc2c(c1)C(=Cc1cc(-c3ccccc3)c[nH]1)C(=O)N2
|
237 |
-
CC1CN(C(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c2ccccc21
|
238 |
-
NCC1(Cc2ccc(Cl)cc2)CCN(c2ncnc3[nH]cnc23)CC1
|
239 |
-
CC1SCc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c21
|
240 |
-
CN1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
241 |
-
COc1cc2ncnc3c2cc1OCCCCCN(C)Cc1ccc(Br)cc1N3
|
242 |
-
Cc1c(C(=O)O)c[nH]c1C=C1C(=O)Nc2ccc(NC(N)=O)cc21
|
243 |
-
Cc1noc(C)c1S(=O)(=O)NCC(O)CN1CCCC2(CCN(c3ncnc(N)c3C3CC3)C2)C1
|
244 |
-
O=c1[nH]ccc2nc(-c3ccc(CN4CCC(c5nnc(-c6ncccn6)[nH]5)CC4)cc3)c(-c3ccccc3)cc12
|
245 |
-
NC1(c2ccc(-c3nc4c(Br)cccn4c3-c3ccccc3)cc2)CCC1
|
246 |
-
Nc1ccc(C2OC3CC(=O)OC3C3=C2C(=O)c2ccccc2C3=O)cc1
|
247 |
-
Nc1ncccc1-c1nc2cccnc2n1-c1ccc(CNC(=O)Cc2ccccc2)cc1
|
248 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(c4ccc(Cl)cc4)C4CCCCN4)CC3)c21
|
249 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c(C(N)=O)[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2n1
|
250 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4cc(Cl)cc(Cl)c4)c3)cc12
|
251 |
-
CC(C)(C)c1ccc(CNC(=O)C2(N)CCN(c3ncnc4[nH]ccc34)CC2)cc1
|
252 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CN(CCCCN)C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(=O)N(CCN)CC(N)=O
|
253 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3cc(F)ccc3O)cc12
|
254 |
-
Cc1[nH]nc2ccc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)cc12
|
255 |
-
Cn1nccc1-c1ccc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)cc1
|
256 |
-
Nc1ccccc1-c1nc2ccc(-c3ccccc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
257 |
-
CCOCCN(CC(O)CN1CCCC2(CCc3cc(F)ccc3O2)C1)S(=O)(=O)c1c(C)cccc1C
|
258 |
-
CC(C)(Cc1ccc(Cl)cc1Cl)C1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
259 |
-
O=c1[nH]c2ccccc2n1C1CCN(Cc2ccc(-c3ncc(-c4nnn[nH]4)cc3-c3ccccc3)cc2)CC1
|
260 |
-
Cl.NCc1ccc(-n2c(-c3cccnc3N)nc3cc(Br)cnc32)cc1
|
261 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)cc3)cn2CCN2CCCC2)CC1
|
262 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12
|
263 |
-
Cc1noc(C2CCCN(Cc3ccc(-c4nnc5n4-c4cccnc4Nc4ccccc4-5)cc3)C2)n1
|
264 |
-
NC1(C(=O)NC(CCCO)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
265 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3)CC1)c1ccc(Cl)cc1
|
266 |
-
O=c1[nH]c2ccccc2n1C1CCN(Cc2ccc(-c3nc4cc5cn[nH]c5cc4nc3-c3ccccc3)cc2)CC1
|
267 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc(OC(F)(F)F)c1
|
268 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc(C(F)(F)F)c1O
|
269 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(Cl)c(Cl)c4)c3)cc12
|
270 |
-
CCNc1nc(-c2ccoc2)c(-c2cnc3[nH]nc(C)c3n2)cc1OCC(N)Cc1ccccc1
|
271 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
272 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CCN)cc21
|
273 |
-
Cc1cccc(-c2nc(C3CCN(Cc4ccc(-c5nc6nccn6cc5-c5ccc(F)cc5)cc4)CC3)n[nH]2)n1
|
274 |
-
Sc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
275 |
-
O=C1Cc2cc(NC(COc3cncc(-c4ccc5c(c4)CC(=O)N5)c3)Cc3c[nH]c4ccccc34)ccc2N1
|
276 |
-
Cc1c(Cl)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1Br
|
277 |
-
c1ccc(OC2CCCN(Cc3ccc(-c4nnc5n4-c4cccnc4Nc4ccccc4-5)cc3)C2)cc1
|
278 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)c(Br)c1
|
279 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CN(CCCN)C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(=O)N(CCN)CC(N)=O
|
280 |
-
C=Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(C(F)(F)F)cc3)cn2CCN2CCCC2)CC1
|
281 |
-
NC1(C(=O)NC(CCO)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
282 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(c4ccc(Cl)cc4)C4COCCN4)CC3)c21
|
283 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCN3CCCC3)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
284 |
-
CNCCn1cc(-c2ccc(F)c(C)c2)nc1C1CCN(c2ncnc(N)c2-c2cn[nH]c2)CC1
|
285 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12
|
286 |
-
NC1(c2ccc(-c3nc4cc(-c5cn[nH]c5)ccn4c3-c3ccccc3)cc2)CCC1
|
287 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3)CC1)c1ccc(Cl)cc1
|
288 |
-
CCNCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
289 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCO)CC1
|
290 |
-
CCN(CCn1cc(-c2ccc(F)c(C)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1)C(C)C
|
291 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2cccc(F)c2)oc1Cl
|
292 |
-
CC1Cc2c(ccc(F)c2Cl)N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
293 |
-
CC(C)CCCC1(C)CCc2ccc(O)c(O)c2O1
|
294 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CCCOCC2)cn1
|
295 |
-
Cn1cc(-c2c(Cl)cnn2C)cc1C(=O)NC1CNCCC1c1ccc(Cl)cc1
|
296 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(C(F)(F)F)c(F)c3)cn2CCN2CCCC2)CC1
|
297 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CCN(C3CC3)CC2)cn1
|
298 |
-
Nc1ccc(-n2c(-c3cccnc3N)nc3cccnc32)cc1
|
299 |
-
CC1(O)CC(O)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cc(C(=O)O)ccc3-4)cc2)C1
|
300 |
-
NC1(C(=O)NC(CCO)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
301 |
-
COC(=O)c1ccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)cn1
|
302 |
-
NC1(c2ccc(-n3c(-c4ccnnc4)nc4ccc(-c5cccc(N6CCOCC6)c5)nc43)cc2)CCC1
|
303 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)CC4CCCCC4)c3)cc12
|
304 |
-
Nc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(n6cnc(-c7ccccn7)c6)CC5)cc4)nc3ccn12
|
305 |
-
NC1CCN(c2ncnc3[nH]cc(Cl)c23)C1
|
306 |
-
CCNCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2OC(C)C)CC1
|
307 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CC(F)C2)CC1
|
308 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccc(Cl)cc4Cl)s3)cc12
|
309 |
-
Cc1n[nH]c2ccc(-c3cncc(OCCCC(N)Cc4ccccc4)c3)cc12
|
310 |
-
Cn1cccc1CC(C)(C)C1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
311 |
-
NCC(c1ccccc1)c1cncc(C=Cc2ccncc2)c1
|
312 |
-
O=c1[nH]ccc2nc(-c3ccc(CN4CCC(c5nnc(-c6cnccn6)[nH]5)CC4)cc3)c(-c3ccccc3)cc12
|
313 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCOc2c(Cl)cccc21
|
314 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCNC3)c21
|
315 |
-
C=Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
316 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2CCC2CC2)CC1
|
317 |
-
Cn1ncc(Br)c1-c1ccc(C(=O)NC2CNCCC2c2ccc(Cl)cc2)o1
|
318 |
-
NC1(c2ccc(-c3nc4ccc(C(=O)NCCF)cn4c3-c3ccccc3)cc2)CCC1
|
319 |
-
CN(Cc1ccc(Cl)cc1)C(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1
|
320 |
-
CC1Cc2c(ccc(F)c2F)N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
321 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccccc4Cl)c3)cc2s1
|
322 |
-
COc1cc(C(N)=O)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
323 |
-
Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(CC(=O)Nc2ccccc2)cc1
|
324 |
-
Cn1ccc(S(=O)(=O)NCc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)n1
|
325 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCNCC3)c21.O=C(O)C(F)(F)F
|
326 |
-
CCONC(=O)c1ccc2c(c1)OCn1c-2nc(-c2ccc(C3(N)CC(C)(O)C3)cc2)c1-c1ccccc1
|
327 |
-
NC(COc1cncc(-c2ccc3cnc(-c4ccccc4)cc3c2)c1)Cc1c[nH]c2ccccc12
|
328 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(c5nnc(-c6ccccn6)[nH]5)CC4)cc3)nc2n1
|
329 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccnc(C(F)(F)F)c4)cn3CCNC3CC3)CC2)c1Cl
|
330 |
-
Cl.NCc1ccc(-n2c(-c3cccnc3N)nc3ccc(-c4cn[nH]c4)nc32)cc1
|
331 |
-
Cn1c(=O)[nH]c2ccc(-c3cnc(NCC(N)Cc4ccc(C(F)(F)F)cc4)s3)cc21
|
332 |
-
Fc1ccc(-c2cn3ccnc3nc2-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)cc1
|
333 |
-
Cc1cc(CC(N)COc2cncc(-c3cc4c(C)n[nH]c4cn3)c2)ccc1F
|
334 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
335 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4cccc(Br)c4)c3)cc12
|
336 |
-
Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
337 |
-
O=C(C(CNC1CCCC1)c1ccc(Cl)cc1)N1CCN(c2ncnc3sc4c(c23)CCC4)CC1
|
338 |
-
CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CCN(CC4CC4)CC3)cn2)cs1)C(F)(F)F
|
339 |
-
CC(O)CNC(=O)c1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(C5(N)CCC5)cc4)c3n2)c1
|
340 |
-
CC(=C1C(=O)Nc2ccc(NC(N)=O)cc21)c1cc(CNC(=O)C2CCNCC2)c[nH]1
|
341 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccc(F)c(F)c4)s3)cc12
|
342 |
-
NC1(c2ccc(-c3nc4ccc(C(=O)NC5CC5)cn4c3-c3ccccc3)cc2)CCC1
|
343 |
-
CS(=O)(=O)NCCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
344 |
-
NC(COc1cnc(Cl)c(-c2ccc3cnccc3c2)c1)Cc1c[nH]c2ccccc12
|
345 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNCC4CC4)c4ccc(Cl)cc4)CC3)c21
|
346 |
-
NC(CNc1cncc(Oc2cccc3cnccc23)c1)Cc1c[nH]c2ccccc12
|
347 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNCC4CCOCC4)c4ccc(Cl)cc4)CC3)c21
|
348 |
-
Nc1ncnc(N2CCC(c3nc(-c4cccc(F)c4)cn3CCN3CCCC3)CC2)c1Cl
|
349 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3cccc(C(F)(F)F)c3)cn2CCN2CCCC2)CC1
|
350 |
-
NC(COc1cnc2ccc(-c3ccncc3)cc2c1)Cc1c[nH]c2ccccc12
|
351 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3cccc(F)c3)cn2CCN2CCCC2)CC1
|
352 |
-
NC(=O)c1cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2cn1
|
353 |
-
NC1(C(=O)NC(CCN2CCCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
354 |
-
Cc1cnc2c(c1)OCn1c-2nc(-c2ccc(C3(N)CC(C)(O)C3)cc2)c1-c1ccccc1
|
355 |
-
NC(=O)C=Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
356 |
-
NC(CNc1nnc(-c2ccc3cnccc3c2)s1)Cc1ccc(C(F)(F)F)cc1
|
357 |
-
CCn1c(-c2nonc2N)nc2cncc(OC3CCNCC3)c21
|
358 |
-
NC(COc1cncc(-c2ccc3[nH]nc(N4CCOCC4)c3c2)c1)Cc1c[nH]c2ccccc12
|
359 |
-
Cc1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn2c1Br
|
360 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OC(CCN)c3ccccc3)cc21
|
361 |
-
CC(=O)N1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
362 |
-
c1ccc(-c2cc(-c3nn[nH]n3)cnc2-c2ccc(CNCc3ccc(-c4csnn4)cc3)cc2)cc1
|
363 |
-
COc1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6cc[nH]c(=O)c6cc5-c5ccccc5)cc4)CC3)[nH]2)cn1
|
364 |
-
CCCC1NC(=O)C(CCCNC(=N)N)NC(=O)CN(C(=O)C(N)CCCNC(=N)N)CCCCCCNC(=O)NCCCCN(CC(N)=O)C(=O)C(CCC(C)C)NC(=O)C(CN)NC(=O)C(Cc2ccc(O)cc2)NC1=O
|
365 |
-
CN(C)CCN1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7cc[n+]([O-])cc7)[nH]6)CC5)cc4)nc3n2)CC1
|
366 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCC3)CC2)c1-c1ccc(F)cc1
|
367 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(C7CC7)nn6cc5-c5ccccc5)cc4)C3)n[nH]2)n1
|
368 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1cc2c(C)n[nH]c2cn1
|
369 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3F)CC1)c1ccc(Cl)cc1
|
370 |
-
CC(C)(C)c1ccc(CC2(N)CCN(c3ncnc4[nH]c(=O)[nH]c34)CC2)cc1
|
371 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4cccc(OCCC5CCNCC5)c4)c3)cc12
|
372 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4cccc(OCC5CCNCC5)c4)c3)cc12
|
373 |
-
COC(=O)c1cccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)c1
|
374 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(F)cc4)C3)c12
|
375 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(F)c(CO)c5)cnc4cc3-c3ccccc3)cc2)CCC1
|
376 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccccc4Cl)s3)cc12
|
377 |
-
NC1(c2ccc(-c3nc4ccn5c(=O)[nH]nc5c4cc3-c3ccccc3)cc2)CCC1
|
378 |
-
Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
379 |
-
CCOCCN(CC(O)CN1CCCC2(CCN(c3ncnc(N)c3C3CC3)C2)C1)S(=O)(=O)c1c(C)cccc1C
|
380 |
-
O=C(NC(c1ccc(Cl)c(F)c1)C1CCNCC1)c1ccc2cnccc2c1
|
381 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccccc1OCCN1CCCC1
|
382 |
-
Cc1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
383 |
-
Nc1ncccc1-c1nc2ccc(Nc3ccccc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
384 |
-
NC(=O)COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
385 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(C(F)(F)F)c(F)c3)cn2CCN2CCCC(F)(F)C2)CC1
|
386 |
-
CCCC1OC2CC(=O)OC2C2=C1C(=O)c1c(OC)cccc1C2=O
|
387 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
388 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3O)CC1)c1ccc(Cl)cc1
|
389 |
-
Cc1c(CCC(=O)O)c[nH]c1C=C1C(=O)Nc2ccc(NC(N)=O)cc21
|
390 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1cnc2ccccc2c1
|
391 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(-c3ccccc3)cccc21
|
392 |
-
NC1(c2ccc(-c3nc4c5ccc(-c6ccc(O)nc6)cc5nn4cc3-c3ccccc3)cc2)CCC1
|
393 |
-
NC1(c2ccc(-c3nc4ccc5nnc(C6NCNN6)n5c4cc3-c3ccccc3)cc2)CC(O)(C2CC2)C1
|
394 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccccc1
|
395 |
-
NC(COc1cncc(-c2cc3c(Cl)n[nH]c3cn2)c1)Cc1cccc(C(F)(F)F)c1
|
396 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6ncc(C)n6cc5-c5ccccc5)cc4)C3)n[nH]2)n1
|
397 |
-
NC1(C(=O)NC(CCCN2CCCCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
398 |
-
COc1ccc(-c2nc3c(C)nc(N)nc3n(C3CCC(O)CC3)c2=O)cn1
|
399 |
-
O=C1NCN(c2ccccc2)C12CCN(Cc1ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc1)CC2
|
400 |
-
O=C1Nc2ccc(NC(COc3cncc(-c4ccc5c(c4)C(=O)C(=O)N5)c3)Cc3c[nH]c4ccccc34)cc2C1=O
|
401 |
-
CC(C)CCCC1(C)CCc2cc(N)ccc2O1
|
402 |
-
CN(C)CCCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
403 |
-
O=c1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6cc[nH]c(=O)c6cc5-c5ccccc5)cc4)CC3)[nH]2)c[nH]1
|
404 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCO)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
405 |
-
CC(C)NCC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3c2C(C)SC3)CC1
|
406 |
-
COc1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
407 |
-
c1ccc(-c2cc3cnccc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
408 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCOc2ccccc21
|
409 |
-
Cc1c[nH]c2ncnc(N3CC4(CCNCC4)c4ccccc43)c12
|
410 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2ccc(Cl)c(C(F)(F)F)c2)sc1Cl
|
411 |
-
Cn1c(CC(=O)Nc2ccc(F)c(C(F)F)c2)nc(N2CCOCC2)cc1=O
|
412 |
-
C=CC1CCc2ncnc(N3CCN(C(=O)C(CNC(C)C)c4ccc(Cl)cc4)CC3)c21
|
413 |
-
COc1ccc(CC(N)C(=O)N2CCN(c3ncnc4ccccc34)CC2)cc1
|
414 |
-
Nc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
415 |
-
CC1=NN(C(=O)c2ccc(Cl)cc2)C(=O)C1N=Nc1ccc(S(=O)(=O)Nc2ncccn2)cc1
|
416 |
-
CC(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CC(=O)Nc5cccc(F)c5)cc4)c3n2)c1
|
417 |
-
CCOCCN(CC(O)CN1CCCC2(CCN(c3ncnc4ccccc34)C2)C1)S(=O)(=O)c1c(C)cccc1C
|
418 |
-
Cc1cc(-c2ccc3nc(-c4ccc(C5(N)CCC5)cc4)c(-c4ccccc4)n3c2)[nH]n1
|
419 |
-
c1ccc(-c2cc3c(ccn4cnnc34)nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
420 |
-
Cc1ccnc2c1nc(-c1cccnc1N)n2-c1ccc(CN)cc1.Cl
|
421 |
-
O=c1[nH]c2ccccc2n1C1CCN(Cc2ccc(-c3nc4ccc(-n5cnnn5)cc4nc3-c3ccccc3)cc2)CC1
|
422 |
-
CCCCCCCCCCCCCCCC(=O)OCC(COP(=O)(O)OC1C(O)C(OP(=O)(O)O)C(OP(=O)(O)O)C(OP(=O)(O)O)C1O)OC(=O)CCCCCCCCCCCCCCC
|
423 |
-
COc1ncc(-c2cc3c(C)nc(N)nc3n(C3CCC(OCC(N)=O)CC3)c2=O)cc1F
|
424 |
-
CC(C)CCCC1(C)CCc2cc(S(N)(=O)=O)cc(F)c2O1
|
425 |
-
CCc1cnn(C)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)s1.Cl
|
426 |
-
Cl.Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(C5(N)CCC5)cc4)c3n2)c1
|
427 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3O)CC1)c1ccc(Cl)cc1
|
428 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCNCC3)c21.O=C(O)C(F)(F)F
|
429 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4cccc(F)c4)C3)c12
|
430 |
-
CNC(=O)CC1CC(c2ccc(Cl)c(Cl)c2)C(NC(=O)c2cc(-c3c(Cl)cnn3C)c(Cl)o2)CN1
|
431 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1c[nH]c2ccccc12
|
432 |
-
COc1ncc(-c2cc3c(C)nc(N)nc3n(C3CCC(OCC(N)=O)CC3)c2=O)cc1F
|
433 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(C)c(Br)n6cc5-c5ccccc5)cc4)C3)n[nH]2)n1
|
434 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(C)nn6c(C)c5-c5ccccc5)cc4)C3)n[nH]2)n1
|
435 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccnc(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1Br
|
436 |
-
Cc1n[nH]c2ncc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)c(C#N)nc3-c3ccoc3)nc12
|
437 |
-
[C-]#[N+]c1ccc(C(=O)N2CCN(Cc3ccc(-c4nnc5n4-c4cccnc4Nc4ccccc4-5)cc3)CC2)cc1
|
438 |
-
Cc1cnc2cc(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nn12
|
439 |
-
N=C(c1ccccc1)n1c(=N)ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)cc21
|
440 |
-
c1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nnc6n5-c5cccnc5Nc5ccccc5-6)cc4)CC3)o2)cc1
|
441 |
-
NC1(c2ccc(-c3nc4cc(C(=O)NO)ccn4c3-c3ccccc3)cc2)CCC1
|
442 |
-
OCCN1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
443 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccnc(C)c3)cn2CCN2CCC2)CC1
|
444 |
-
Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(CC(=O)Nc2ccccc2)cc1
|
445 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(F)c3)cn2CCN2CC(F)C2)CC1
|
446 |
-
CC(C)CNC(c1ccc(Cl)cc1)c1ccc(-c2ncnc3[nH]cnc23)cc1
|
447 |
-
CCc1cc(OC)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
448 |
-
Cl.Nc1ncccc1-c1nc2ccc(-c3ccccc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
449 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccnc(C(F)(F)F)c3)cn2CCN2CCC2)CC1
|
450 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCCC3)CC2)c1-c1ccccc1F
|
451 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4cccc(Cl)c4)c3)cc2s1
|
452 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(OC(F)F)cc3)cn2CCN2CCC2)CC1
|
453 |
-
CCOC(=O)c1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
454 |
-
NCC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3[nH]ccc23)CC1
|
455 |
-
COc1nc(C(N)=O)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
456 |
-
O=S(=O)(NCCNCCOCc1ccc(Cl)cc1)c1cccc2cnccc12
|
457 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2cccc(F)c2)oc1Cl
|
458 |
-
Cn1c(CC(=O)N2CCc3c(F)cccc32)nc(N2CCOCC2)cc1=O
|
459 |
-
COC(=O)COc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
460 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCc4nnc(N)s4)cc3)nc2n1
|
461 |
-
Cc1nc2nc(-c3ccc(CN4CCC(c5n[nH]c(-c6cc(Cl)ccn6)n5)CC4)cc3)c(-c3ccc(F)cc3F)cn2n1
|
462 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(S(C)(=O)=O)cc1
|
463 |
-
CCc1n[nH]c2ncnc(N3CCN(c4cc(Cl)cc(NCCN5CCCC5)c4C)CC3)c12
|
464 |
-
Cc1n[nH]c2cnc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)cc12
|
465 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC3CCNCC3)c21.O=C(O)C(F)(F)F
|
466 |
-
COc1ccccc1C(=O)N1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
467 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2ccccc2)oc1Cl.O=C(O)C(O)C(O)C(=O)O
|
468 |
-
Cc1nc2nc(-c3ccc(CN4CCC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn2n1
|
469 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CCC(F)CC2)cn1
|
470 |
-
Cl.Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(C5(N)CCC5)cc4)c3n2)c1
|
471 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cccc(F)c3-4)cc2)CCC1
|
472 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4c[nH]c5cc(F)ccc45)cnc3-c3ccoc3)cc12
|
473 |
-
COC(=O)c1cc(Cl)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
474 |
-
Nc1ncccc1-c1nc2cc(C3CCCC3)cnc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
475 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(Cl)c(F)c4)c3)cc12
|
476 |
-
Cn1c(CC(=O)Nc2ccc(F)c(C3CC3)c2)nc(N2CCOCC2)cc1=O
|
477 |
-
NCC1(Cc2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
478 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(S(N)(=O)=O)cc5)cnc4cc3-c3ccccc3)cc2)CCC1
|
479 |
-
CN(C)C(=O)c1cccc(-c2ccc3nc(-c4ccccc4)n(-c4ccc(C5(N)CCC5)cc4)c3n2)c1
|
480 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(c5nnc(N)s5)CC4)cc3)nc2n1
|
481 |
-
NC(Cc1ccc(F)c(F)c1)C(=O)N1CCN(c2ncnc3ccccc23)CC1
|
482 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(c4ccc(Cl)cc4)C4COCCN4)CC3)c21
|
483 |
-
CCC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cccnc3-4)cc2)C1
|
484 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
485 |
-
Cc1ccc(-c2nc3c(C)nc(N)nc3n(C3CCOCC3)c2=O)cn1
|
486 |
-
CN(C)CCN1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ncccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
487 |
-
Nc1cc2cc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)ccc2cn1
|
488 |
-
CC1CN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC(C)O1
|
489 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccccc1F
|
490 |
-
Cc1cc(O)cc2c1NC(C)(CCCC(C)C)CC2
|
491 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(C)N)cc21
|
492 |
-
CC1Cc2c(Br)cccc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)n1C
|
493 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4ccc(F)c(F)c4F)c3)cc12
|
494 |
-
Nc1ncnc(N2CCC(c3nc(-c4cccc(F)c4)cn3CCN3CCCC3)CC2)c1Br
|
495 |
-
CCOC(=O)c1cncc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)c1
|
496 |
-
CC(C)CCCC1(C)CCc2cc(O)cc(Br)c2O1
|
497 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNCC4CCOCC4)c4ccc(Cl)cc4)CC3)c21
|
498 |
-
CC(C)NCC(Cc1ccc(Cl)c(F)c1)C(=O)N1CCN(c2ncnc3c2C(C)SC3)CC1
|
499 |
-
CCOC(=O)c1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
500 |
-
COc1cc2ncc3c(N)nc(-c4cncc(OCC(N)Cc5ccccc5)c4)cc3c2cc1OC
|
501 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1Cl
|
502 |
-
COc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C)c3)cn2CC2CNC2)CC1
|
503 |
-
Cn1nccc1-c1oc(C(=O)NC2CNCCC2c2ccc(Cl)cc2)cc1Br
|
504 |
-
NC(=O)Nc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
505 |
-
Cc1ccc(S(=O)(=O)NC2(c3ccc(-c4nnc5n4-c4cccnc4Nc4ccccc4-5)cc3)CCC2)cc1
|
506 |
-
NC(=O)C1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
507 |
-
NC1(c2ccc(-c3nc4nc(-n5ccccc5=O)ccn4c3-c3ccccc3)cc2)CCC1
|
508 |
-
c1ccc(-c2cc3cccnc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
509 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ncccc3-4)cc2)CCC1
|
510 |
-
NC1(c2ccc(-c3nc4cc(-c5ccncc5)ccn4c3-c3ccccc3)cc2)CCC1
|
511 |
-
CNc1nccc(-c2ccc(C(=O)NC(CN)Cc3ccc(Cl)cc3Cl)s2)n1
|
512 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCCN)cc21
|
513 |
-
CC1Cc2cc(F)c(F)cc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)n1C
|
514 |
-
O=c1[nH]c2ccccc2n1C1CCN(Cc2ccc(-c3nc4cc(-n5cnnn5)ccc4nc3-c3ccccc3)cc2)CC1
|
515 |
-
COc1cc(COc2ccn3c(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc3n2)ccn1
|
516 |
-
O=S(=O)(NCc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1cccnc1
|
517 |
-
NC1(c2ccc(-c3nc4ccc(-c5cnc[nH]5)cn4c3-c3ccccc3)cc2)CCC1
|
518 |
-
C#Cc1cc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)ccc1F
|
519 |
-
CCc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(F)cc4F)C3)c12
|
520 |
-
COC(=O)COc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
521 |
-
CC1OC2OC(=O)OC2C2=C1C(=O)c1c(O)cccc1C2=O
|
522 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc5c(c4)OCO5)c3)cc12
|
523 |
-
Nc1cc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCC3)CC2)ncn1
|
524 |
-
Nc1cc2cc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)ccc2cn1
|
525 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN)CC1
|
526 |
-
CNc1c(-c2ccccc2)c(-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)nc2nc(C)cn12
|
527 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3ccccc3O)cc12
|
528 |
-
NC(=O)c1cc(Br)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
529 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccccc3)cn2CCN2CCC2)CC1
|
530 |
-
C=CC1CCc2ncnc(N3CCN(C(=O)C(CNC(C)C)c4ccc(Cl)cc4)CC3)c21
|
531 |
-
N#CCc1ccc(-n2cnc3cnc4ccc(C#Cc5cccnc5)cc4c32)cc1
|
532 |
-
CN(C)CC(NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
533 |
-
CCn1c(-c2nonc2N)nc2c(C#CC3CC3)ncc(OCCCN)c21
|
534 |
-
CC1Cc2cc(F)c(F)cc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)[nH]1
|
535 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CN3CCCC3)cc21
|
536 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1c[nH]c2ccccc12
|
537 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3ccccc3)cc12
|
538 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc(OC(F)F)c1
|
539 |
-
CCC1CN(c2cc(=O)[nH]c(CC(=O)Nc3ccc(F)cc3)n2)CCO1
|
540 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccc(-c5ccn[nH]5)cc3-4)cc2)C1
|
541 |
-
NC1(c2ccc(-n3c(-c4ncccn4)nc4ccc(-c5cccc(N6CCOCC6)c5)nc43)cc2)CCC1
|
542 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(F)cc5)cnc4cc3-c3ccccc3)cc2)CCC1
|
543 |
-
CC(=O)Nc1nc2ccc(-c3ccnc(N(C)S(=O)(=O)c4ccccc4F)n3)cc2s1
|
544 |
-
CNc1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn2n1
|
545 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3cccc(C(F)(F)F)c3)cn2CCN2CCCC2)CC1
|
546 |
-
Cc1cccc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c1F
|
547 |
-
Cn1c(CC(=O)Nc2ccc(F)c(Br)c2)nc(N2CCOCC2)cc1=O
|
548 |
-
Cn1cnc(S(=O)(=O)NCc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)c1
|
549 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2cc(Cl)ccc21
|
550 |
-
Cc1cc(-c2cn[nH]c2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
551 |
-
Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
552 |
-
Nc1ncccc1-c1nc2ccc(Sc3ccccc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
553 |
-
CSc1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn2n1
|
554 |
-
CC(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(C5(N)CCC5)cc4)c3n2)c1.Cl
|
555 |
-
NC1(C(=O)NCc2ccc(F)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
556 |
-
NCC1CCN(c2ncnc3[nH]ccc23)CC1
|
557 |
-
O=S(=O)(NCc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccc(Cl)cc1
|
558 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCN3CCC(O)CC3)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
559 |
-
CCc1nc2cnc3ccc(C#Cc4cccnc4)cc3c2n1-c1ccc(CC#N)cc1
|
560 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c(C#N)[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2n1
|
561 |
-
COc1cccc(-c2c[nH]c(C=C3C(=O)Nc4ccc(NC(N)=O)cc43)c2)c1
|
562 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(Cl)c4)cn3CCN3CCCC3)CC2)c1C1CCC1
|
563 |
-
O=C(N1CCN(c2ncnc3[nH]cc(Cl)c23)CC1)C1(c2ccc(Cl)c(Cl)c2)CCNCC1
|
564 |
-
NC(=O)c1cccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)c1
|
565 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccccc3-4)cc2)C1
|
566 |
-
CCn1c(-c2nonc2N)nc2cncc(CNC3CCNCC3)c21
|
567 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2cc(F)ccc21
|
568 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2cccc(C(F)(F)F)c2)oc1Cl
|
569 |
-
CCOCCN(CC(O)CN1CCCC2(CCN(c3ncnc4[nH]nc(C)c34)C2)C1)S(=O)(=O)c1c(C)cccc1C
|
570 |
-
CCc1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccn1
|
571 |
-
Nc1ncnc(Cl)c1-c1nc2ccc(-c3ccccc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
572 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ncccc3-4)cc2)CCC1
|
573 |
-
NC1(c2ccc(-c3nc4ccccc4cc3-c3ccccc3)cc2)CCC1
|
574 |
-
CNc1c(-c2ccccc2)c(-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)nc2nc(C)cn12
|
575 |
-
COC1CCN(c2cnc(C(=O)Nc3csc(-c4nncn4C4CC4)n3)cc2-n2cnc(C3CC3)c2)C1
|
576 |
-
NC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3ccc(-c4ccccc4)cc23)CC1
|
577 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(CO)cc5)cnc4cc3-c3ccccc3)cc2)CCC1
|
578 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccnc(C(F)(F)F)c3)cn2CCN2CCC2)CC1
|
579 |
-
COC(=O)CCc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
580 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCNC2CCCC2)CC1
|
581 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CNC)cc21
|
582 |
-
N#CC1CN(c2cnc(C(=O)Nc3csc(-c4nncn4C4CC4)n3)cc2-n2cnc(C3CC3)c2)C1
|
583 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCCCN)cc21
|
584 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCNS(C)(=O)=O)CC2c2ccc(F)c(F)c2)oc1Cl
|
585 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc12
|
586 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCc4nc[nH]n4)cc3)nc2n1
|
587 |
-
CCC1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
588 |
-
Nc1nc(O)nc2nc(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)c(-c3ccccc3)cc12
|
589 |
-
CN(C)C(=O)c1ccc2c(c1)OCn1c-2nc(-c2ccc(C3(N)CC(C)(O)C3)cc2)c1-c1ccccc1
|
590 |
-
CC(=O)c1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
591 |
-
NC1(c2ccc(-c3nc4cc(-c5cc[nH]n5)ccn4c3-c3ccccc3)cc2)CCC1
|
592 |
-
CC1SCc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(F)cc4)CC3)c21
|
593 |
-
CCc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
594 |
-
O=C(NC(c1cccc(Cl)c1Cl)C1CCNCC1)c1ccc2cnccc2c1
|
595 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)c[nH]3)CC2)c1Br
|
596 |
-
Cc1nnc(-c2ccccc2Nc2ncnc3[nH]ccc23)[nH]1
|
597 |
-
O=C(NCc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccccc1
|
598 |
-
C=CC(=O)C(Cc1ccccc1)NC(=O)OC(C)(C)C
|
599 |
-
Cc1ccc(-c2ccc3nn4cc(-c5ccccc5)c(-c5ccc(C6(N)CCC6)cc5)nc4c3c2)cc1
|
600 |
-
NC1(C(=O)NC(c2ccc(Cl)cc2)C2CC2)CCN(c2ncnc3[nH]ccc23)CC1
|
601 |
-
Cn1c(CC(=O)N2CCc3c(Cl)cccc32)nc(N2CCOCC2)cc1=O
|
602 |
-
CC(C)(Cc1ccccn1)C1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
603 |
-
COc1ncc(-c2cc3c(C)nc(N)nc3n(C3CCC(OCO)CC3)c2=O)cn1
|
604 |
-
COCCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
605 |
-
CCOC(=O)c1c(C)nc(NNC(=O)c2cccc3c(=O)c4ccccc4[nH]c23)nc1-c1ccc(OC)cc1
|
606 |
-
CCOCCN(CC(O)CN1CCCC2(CC(=O)c3cc(O)ccc3O2)C1)S(=O)(=O)c1ccccc1Cl
|
607 |
-
NC1(C(=O)NC(c2ccccc2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
608 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3sc4c(c23)CCC4)CC1)c1ccc(Br)cc1
|
609 |
-
CC(C)(Cc1ccccc1)C1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
610 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(NC(=O)CNC(C)=O)CC4)cc3)nc2n1
|
611 |
-
Cc1n[nH]c2cnc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)nc12
|
612 |
-
NC(COc1cncc(-c2ccc3[nH]ncc3c2)c1)Cc1c[nH]c2ccccc12
|
613 |
-
NC1(Cc2ccc(Cl)cc2Cl)CCN(c2ncnc3[nH]ccc23)CC1
|
614 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(C(F)(F)F)c(F)c3)cn2CCN2CCC(F)CC2)CC1
|
615 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
616 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CCN)cc21
|
617 |
-
Cc1ccc2c(c1)Nc1ncccc1-n1c(-c3ccc(C4(N)CCC4)cc3)nnc1-2
|
618 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(CF)CC3)CC1)c1ccc(Cl)cc1
|
619 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)n1C1CC1)N1CCc2c(Cl)cccc21
|
620 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOC(CF)C2)cc(=O)[nH]1
|
621 |
-
NC1(c2ccc(-c3nc4nc(Oc5ccccc5)ccn4c3-c3ccccc3)cc2)CCC1
|
622 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCCC2)CC1
|
623 |
-
CCNC(=O)Nc1ccc(CNc2ncsc2C(=O)Nc2ccc3c(c2)OC(F)(F)O3)cn1
|
624 |
-
COc1nn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CC(c5n[nH]c(-c6cccc(C)n6)n5)C4)cc3)nc2c1CO
|
625 |
-
Cc1cnc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
626 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=S)[nH]1
|
627 |
-
CN1CCN(c2nccc3nc(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)c(-c4ccccc4)cc23)CC1
|
628 |
-
CC(C)(C)c1cccc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c1
|
629 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)Cc3ccccc3)cc21
|
630 |
-
CC(C)CNCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
631 |
-
O=C(O)c1ccc2nc(-c3ccccc3)c(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)nc2c1
|
632 |
-
CC1COCCN1c1nc(N2CCOCC2C)c2ccc(-c3ccc4[nH]nc(N)c4c3)nc2n1
|
633 |
-
CCn1c(-c2nonc2N)nc2c(-c3ccoc3)ncc(OCCCN)c21
|
634 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c(Cl)[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2n1
|
635 |
-
Cn1c(CC(=O)N2CCc3ccccc32)nc(N2CCOCC2)cc1=O
|
636 |
-
CC1SCc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(F)c(F)c4)CC3)c21
|
637 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccc(C(C)(C)C)cc4)c3)cc2s1
|
638 |
-
NC1(CNC(=O)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
639 |
-
NC(=O)C1(c2ccc(-n3c(-c4cccnc4N)nc4ccc(-c5ccccc5)nc43)cc2)CCC1
|
640 |
-
Cl.Nc1ncccc1-c1nc2ccc(-c3ccccc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
641 |
-
Cc1cc(-c2ccc3c(c2)nn2cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc32)[nH]n1
|
642 |
-
NC(COc1cncc(-c2ccc3nnccc3c2)c1)Cc1c[nH]c2ccccc12
|
643 |
-
Cc1[nH]nc2ccc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)cc12
|
644 |
-
NC1(c2ccc(-c3nc4c(-c5cn[nH]c5)cccn4c3-c3ccccc3)cc2)CCC1
|
645 |
-
CC(C)Oc1cccc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c1
|
646 |
-
CCC(N)COc1cncc(-c2cc3c(cnc4cc(OC)c(OC)cc43)c(N)n2)c1
|
647 |
-
Nc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
648 |
-
O=S(=O)(NCc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccccc1F
|
649 |
-
NC1(c2ccc(-c3nc4ncc(-c5ccccc5)cn4c3-c3ccccc3)cc2)CCC1
|
650 |
-
CCOC(=O)c1cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2cn1
|
651 |
-
O=C(Cc1nc(N2CCOCC2)c(F)c(=O)[nH]1)N1c2ccccc2CC1CO
|
652 |
-
CCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
653 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNC4CCCCC4)c4ccc(Cl)cc4)CC3)c21
|
654 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3cncc(Cl)c3)cn2CCN2CCC2)CC1
|
655 |
-
NC(=O)c1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(c5nnc(-c6ccccn6)[nH]5)CC4)cc3)nc2n1
|
656 |
-
N#CCn1c(O)nc2ccc(NC(COc3cncc(-c4ccc5nc(O)n(CC#N)c5c4)c3)Cc3c[nH]c4ccccc34)cc21
|
657 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCC2c2ccc(C(F)(F)F)cc2)oc1Cl
|
658 |
-
CCOc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C)c3)cn2CCN(CC)CC)CC1
|
659 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3cccc(O)c3)cc12
|
660 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(O)c(C(F)(F)F)c4)cn3CCN3CCCC3)CC2)c1Cl
|
661 |
-
Cl.Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
662 |
-
CC1(C)CN(C(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c2cccc(F)c21
|
663 |
-
NCC(O)(c1ccc(Cl)cc1)c1ccc(-c2cn[nH]c2)cc1
|
664 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
665 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccccc4)s3)cc12
|
666 |
-
NC(CNc1cncc(Nc2cccc3cnccc23)c1)Cc1c[nH]c2ccccc12
|
667 |
-
c1ccc(-c2cc3ncccc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
668 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)C1CCCN1C(=O)C(CCCNC(=N)N)NC)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(N)=O
|
669 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccnc(C(F)(F)F)c3)cn2CCN2CCC2)CC1
|
670 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(C(F)(F)F)c(F)c4)cn3CCN3CCCC3)CC2)c1Cl
|
671 |
-
CCOc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2n1
|
672 |
-
CN1CCN(c2ccc3nc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ncccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
673 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCCC3)CC2)c1-c1ccc(F)cc1
|
674 |
-
NC1(c2ccc(-c3ncc4cccnc4c3-c3ccccc3)cc2)CCC1
|
675 |
-
CC(C)Cc1nc(-c2ccccc2)c(-c2ccc(CN3CCC(n4c(O)nc5ccccc54)CC3)cc2)nc1O
|
676 |
-
COC(=O)C=Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
677 |
-
COC(=O)c1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
678 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(F)cc1
|
679 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNC(C)(C)CO)c4ccc(Cl)cc4)CC3)c21
|
680 |
-
NC(c1ccc(Cl)cc1)c1ccc(-c2ncnc3[nH]cnc23)cc1
|
681 |
-
Cc1ccc(S(=O)(=O)NCc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)cc1
|
682 |
-
NC1(c2ccc(-c3nc4c(Cl)cccn4c3-c3ccccc3)cc2)CCC1
|
683 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CN(CCCCCCN)C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(=O)N(CCCN)CC(N)=O
|
684 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
685 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(F)cc1
|
686 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cc(CO)ccc3-4)cc2)C1
|
687 |
-
C=Cc1cnc2cc(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nn12
|
688 |
-
N=c1ccc2nc(-c3ccc(C4(NC(=O)Cc5cccnc5)CCC4)cc3)c(-c3ccccc3)cc2n1C(N)=O
|
689 |
-
COc1cc2ncc3c(N)nc(-c4cncc(OCC(C)N)c4)cc3c2cc1OC
|
690 |
-
COc1ncc(-c2cc3c(C)nc(N)nc3n(C3CCC(OCC(N)=O)CC3)c2=O)cn1
|
691 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)cc3)cn2CCN2CCCC2)CC1
|
692 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(Cl)c4)cn3CCN3CCCC3)CC2)c1Br
|
693 |
-
O=C(N1CCN(c2ncnc3[nH]ccc23)CC1)C1(c2ccc(Br)cc2)CCNCC1
|
694 |
-
CC(=O)NCC1(N)CCN(c2ncnc3[nH]cc(C)c23)C1
|
695 |
-
O=C1CC2OC(c3ccsc3)C3=C(C(=O)c4ccccc4C3=O)C2O1
|
696 |
-
Nc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
697 |
-
Cl.NCc1ccc(-n2c(-c3cccnc3N)nc3ccc(-c4ccccc4)nc32)cc1
|
698 |
-
COC1CCC(NCC(C(=O)N2CCN(c3ncnc4c3C(C)CC4O)CC2)c2ccc(Cl)cc2)CC1
|
699 |
-
Fc1ccc(-c2cn3c(Cl)cnc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
700 |
-
NC(COc1cncc(-c2ccc3c(c2)C(c2cccnc2)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
701 |
-
CC(C)(C)c1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)ccn1
|
702 |
-
Cc1cc(-c2cn(CC3CNC3)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
703 |
-
N#Cc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
704 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(C(F)(F)F)cc4)c3)cc12
|
705 |
-
NC1(c2ccc(-n3c(-c4ccccc4O)nc4ccc(-c5ccccc5)nc43)cc2)CCC1
|
706 |
-
Cn1cc(CC(N)COc2cncc(-c3ccc4cnccc4c3)c2)c2ccccc21
|
707 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CC3(CCOCC3)C2)cn1
|
708 |
-
O=C(NCCc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccccc1
|
709 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1c2ccccc2CC1CO
|
710 |
-
CNCCn1cc(-c2ccc(F)c(C)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
711 |
-
CC(C)(C)n1nc(Cc2cccc(I)c2)c2c(N)ncnc21
|
712 |
-
c1ccc2c(CC(COc3cncc(-c4cc5cnccc5s4)c3)Nc3cc4cnccc4s3)c[nH]c2c1
|
713 |
-
CN1CCN(c2cccc3c2CCN3C(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)CC1
|
714 |
-
CC(C)CCCC1(C)CCc2c(O)cccc2O1
|
715 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)C1CCCN1C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(N)=O
|
716 |
-
NC(COc1cncc(-c2ccc3c(c2)C(c2ccccn2)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
717 |
-
Cn1ncc(Cl)c1-c1csc(C(=O)NC2(c3ccc(F)cc3)CCNCC2)c1
|
718 |
-
CC(C)Nc1c(-c2ccccc2)c(-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)nc2nc(-c3ccccn3)nn12
|
719 |
-
CC1CN(C(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c2cccc(O)c21
|
720 |
-
Cc1cc(C(=O)NC(CN)c2ccccc2)sc1-c1ccnc2[nH]ccc12
|
721 |
-
COc1cncc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)c1
|
722 |
-
Cn1nnnc1-c1cnc(-c2ccc(CN3CCC(n4c(=O)[nH]c5ccccc54)CC3)cc2)c(-c2ccccc2)c1
|
723 |
-
COc1cc(NS(=O)(=O)c2ccc(NS(=O)(=O)c3c(C)noc3C)cc2)nc(OC)n1
|
724 |
-
O=c1ccc(-c2cc(C3CCN(Cc4ccc(-c5nc6ncccc6cc5-c5ccccc5)cc4)CC3)n[nH]2)c[nH]1
|
725 |
-
NC1(c2ccc(-c3nc4cc(-c5ncc[nH]5)ccn4c3-c3ccccc3)cc2)CCC1
|
726 |
-
NC1(c2ccc(-c3nc4ccc(O)cn4c3-c3ccccc3)cc2)CCC1
|
727 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1c2ccccc2CC1CF
|
728 |
-
CNCCn1cc(-c2ccnc(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2Cl)CC1
|
729 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2C2CNC2)CC1
|
730 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1-c1cnoc1
|
731 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(Cl)c3)cn2CCN2CCC2)CC1
|
732 |
-
Cn1ncc(Cl)c1-c1ccc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)cn1
|
733 |
-
Cc1c(NCCN2CCCC2)cc(Cl)cc1N1CCN(c2ncnc3[nH]nc(Br)c23)CC1
|
734 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2-c2ccc(F)cc2)CC1
|
735 |
-
NCC(NC(=O)c1cc(-c2ccccc2)c(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
736 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3cccc(Cl)c3)cn2CCN2CCC2)CC1
|
737 |
-
NC1(c2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CCC1
|
738 |
-
N#Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(F)cc4F)C3)c12
|
739 |
-
NC(COc1cnc(-c2ccco2)c(-c2ccc3cnccc3c2)c1)Cc1c[nH]c2ccccc12
|
740 |
-
Cc1c(Cl)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1-c1ccn[nH]1
|
741 |
-
Nc1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6cc[nH]c(=O)c6cc5-c5ccccc5)cc4)CC3)[nH]2)cn1
|
742 |
-
COc1ccc(O)c(C(=O)c2ccc(C=CC3CCCNCC3NC(=O)c3ccncc3)cc2)c1F.Cl
|
743 |
-
N#CC1CCN(c2cnc(C(=O)Nc3csc(-c4nncn4C4CC4)n3)cc2-n2cnc(C3CC3)c2)CC1
|
744 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CC3CC3C2)cn1
|
745 |
-
CC1CN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC(C)N1
|
746 |
-
CNc1nccc(-c2ccc(C(=O)NC(CO)Cc3ccc(Cl)cc3Cl)s2)n1
|
747 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCO)CC2c2ccc(F)c(F)c2)oc1Cl
|
748 |
-
C=Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCCCC2)CC1
|
749 |
-
Cc1cc(-c2cccnc2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
750 |
-
N=C(N)NCCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CCCCCNC(=O)C(CCCCN)NC(=O)CCCCCNC(=O)C1OC(n2cnc3c(N)ncnc32)C(O)C1O)C(N)=O
|
751 |
-
NC1(c2ccc(-c3nc4cc(C(=O)N5CCCC5)ccn4c3-c3ccccc3)cc2)CCC1
|
752 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
753 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(F)cc5)c(Cl)nc4cc3-c3ccccc3)cc2)CCC1
|
754 |
-
NC1(C(=O)NCc2ccc(F)cc2Cl)CCN(c2ncnc3[nH]ccc23)CC1
|
755 |
-
OC1CN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CCN1
|
756 |
-
CN(C)CCCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
757 |
-
CCOc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C)c3)cn2CCN2CCCC2)CC1
|
758 |
-
CC(=O)Nc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCC2)CC1
|
759 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNC(C)(C)C)c4ccc(C(F)(F)F)c(F)c4)CC3)c21
|
760 |
-
Cc1nc2cnc3ccc(C#Cc4cccnc4)cc3c2n1-c1ccc(C(C)C#N)cc1
|
761 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCc4n[nH]c(-c5ccccc5)n4)cc3)nc2n1
|
762 |
-
CC(=C1C(=O)Nc2ccc(NC(N)=O)cc21)c1cc(CNC(=O)C2CCN(C)CC2)c[nH]1
|
763 |
-
Cn1cncc1-c1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
764 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21
|
765 |
-
NC(CNc1cnc(Cl)c(C=Cc2ccncc2)c1)Cc1c[nH]c2ccccc12
|
766 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2cccc(F)c2)sc1Cl
|
767 |
-
NC1CCN(c2ccnc3[nH]ccc23)CC1
|
768 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(CO)CC3)CC1)c1ccc(Cl)cc1
|
769 |
-
Cc1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4Br)CC3)n2)ccc1F
|
770 |
-
NC(CNc1ncc(-c2ccc3cn[nH]c3c2)s1)Cc1ccc(C(F)(F)F)cc1
|
771 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)cc1F
|
772 |
-
Cc1cc(-c2cn(CCNCC(C)C)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
773 |
-
c1ccc(-c2nnc[nH]2)c(Nc2ncnc3[nH]ccc23)c1
|
774 |
-
Clc1c[nH]c2ncnc(N3CCc4[nH]cnc4C3)c12
|
775 |
-
Cc1ccc(CC(C)(C)C2C(=O)Nc3ccc(-c4cncc(OCC(N)Cc5c[nH]c6ccccc56)c4)cc32)o1
|
776 |
-
N#Cc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
777 |
-
COC(=O)c1sccc1S(=O)(=O)NC1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
778 |
-
NC1(c2ccc(-c3nc4c5cc(Br)ccc5nn4cc3-c3ccccc3)cc2)CCC1
|
779 |
-
COc1ccc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)cc1Cl
|
780 |
-
CC(C)(C)C(=O)Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1
|
781 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c(C#N)[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2n1
|
782 |
-
COc1cc(OC)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
783 |
-
COc1cc(Cl)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
784 |
-
NC1(c2ccc(-c3nc4cc(C(=O)NO)ccn4c3-c3ccccc3)cc2)CCC1
|
785 |
-
NC1(c2ccc(-n3c(C4CC4)nc4ccc(-c5cccc(N6CCOCC6)c5)nc43)cc2)CCC1
|
786 |
-
CN1CCN(c2ccc3nc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
787 |
-
COC(=O)c1ccc(-c2c(N)ncnc2N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCC3)CC2)cc1
|
788 |
-
COc1cncc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)c1
|
789 |
-
O=c1[nH]ccc2nc(-c3ccc(CN4CCC(c5nnc(-c6cnccn6)[nH]5)CC4)cc3)c(-c3ccccc3)cc12
|
790 |
-
CCOC(=O)c1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
791 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCO)CC2c2ccc(F)c(F)c2)oc1Cl
|
792 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2ccccc21
|
793 |
-
COc1cc2ncc3c(N)nc(-c4cncc(OCC(N)Cc5cccc(C(F)(F)F)c5)c4)cc3c2cc1OC
|
794 |
-
Cn1ncc(Br)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)nc1
|
795 |
-
CCOc1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
796 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OC(CN)c3ccccc3)cc21
|
797 |
-
Cc1ccc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)cc1Cl
|
798 |
-
CC(C)(C)NCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
799 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccsc3)COc3cccc(F)c3-4)cc2)C1
|
800 |
-
COc1ccc(O)c(C(=O)c2ccc(C(=O)NC3CCCNCC3NC(=O)c3ccncc3)cc2)c1F.Cl.Cl
|
801 |
-
NC(Cc1ccc(C(F)(F)F)cc1)C(=O)N1CCN(c2ncnc3ccccc23)CC1
|
802 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCC(=O)c4ccccc4N)cc3)nc2n1
|
803 |
-
Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccc(F)cc4)c3)cc2s1
|
804 |
-
NC1(c2ccc(-c3nc4cc(-c5ccc(F)cc5)ccn4c3-c3ccccc3)cc2)CCC1
|
805 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCCCNCCc3ccc(OC)cc3)c21
|
806 |
-
NC1(c2ccc(-c3nc4c5cc(-c6ccc(S(N)(=O)=O)cc6)ccc5nn4cc3-c3ccccc3)cc2)CCC1
|
807 |
-
CCCCNCC(C(=O)N1CCN(c2ncnc3sc4c(c23)CCC4)CC1)c1ccc(Cl)cc1
|
808 |
-
CC1Cc2cc(F)c(F)cc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
809 |
-
Cn1c(CC(=O)N2CCc3cc(F)c(F)cc32)nc(N2CCOCC2)cc1=O
|
810 |
-
CC(C)NCC(Cc1ccc(F)c(F)c1)C(=O)N1CCN(c2ncnc3c2C(C)SC3)CC1
|
811 |
-
CC(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CN)cc4)c3n2)c1.Cl
|
812 |
-
Cc1cccc(-c2nc(C3CCN(Cc4ccc(-c5nc6nccn6cc5-c5ccccc5F)cc4)CC3)n[nH]2)n1
|
813 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2-c2ccc(N)nc2)CC1
|
814 |
-
Cc1ccc2c(c1)OCn1c-2nc(-c2ccc(C3(N)CC(O)(C4CC4)C3)cc2)c1-c1ccccc1
|
815 |
-
CCOc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CC2CNC2)CC1
|
816 |
-
NC1(C(=O)N2CCCC2c2ccccc2)CCN(c2ncnc3[nH]ccc23)CC1
|
817 |
-
CN1CC(N2CCN(c3ncc4cc(-c5ccccc5)c(-c5ccc(CN6CCC(c7nnc(-c8ccccn8)[nH]7)CC6)cc5)nc4n3)CC2)C1
|
818 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccccc1OCCN1CCCCC1
|
819 |
-
CCc1cnc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
820 |
-
CS(=O)(=O)c1cccc(-c2ccc3c(c2)nn2cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc32)c1
|
821 |
-
C=Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)cc3)cn2CCN2CCCC2)CC1
|
822 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2ccc(F)c(F)c2)oc1Cl
|
823 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(F)c(C(F)(F)F)c4)c3)cc12
|
824 |
-
CCc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
825 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21
|
826 |
-
NC(CCc1cnc(Cl)c(C=Cc2ccncc2)c1)Cc1c[nH]c2ccccc12
|
827 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccccc4C(F)(F)F)c3)cc2s1
|
828 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4cccc(-c5ccccc5)c4)c3)cc12
|
829 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2c1
|
830 |
-
NC1(C(=O)NC(CCCN2CCCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
831 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)C1CCC1
|
832 |
-
CCN(CC)CCn1cc(-c2ccc(F)c(C)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
833 |
-
CC(=C1C(=O)Nc2ccc(NC(N)=O)cc21)c1cc(CNC(=O)CCN2CCCCC2)c[nH]1
|
834 |
-
CC(C)c1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)ccn1
|
835 |
-
Fc1ccc2[nH]c(C3CCN(Cc4ccc(-c5ncc(-c6nn[nH]n6)cc5-c5ccccc5)cc4)CC3)nc2c1
|
836 |
-
O=S(=O)(NC1(c2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CCC1)c1ccc(F)cc1
|
837 |
-
CC(=O)Nc1ccc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c2c1
|
838 |
-
Nc1ncccc1-c1nc2cccnc2n1-c1ccc(CC(=O)Nc2ccccc2)cc1
|
839 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cccc(F)c3-4)cc2)CC(O)(C2CC2)C1
|
840 |
-
Cn1nccc1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)nc1
|
841 |
-
NC1(C(=O)NC(CCN2CCCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
842 |
-
Cc1nc(-c2ccc(CN3CCC(n4c(=O)[nH]c5ccccc54)CC3)cc2)c(-c2ccccc2)[nH]c1=O
|
843 |
-
NC(COc1cncc(-c2ccc3c(c2)C(c2ccccc2)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
844 |
-
Cn1ncc(Br)c1-c1ccc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)cc1
|
845 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)nc12
|
846 |
-
COc1c(N)ncnc1N1CCC(c2nc(-c3ccnc(C(F)(F)F)c3)cn2CCN2CCC2)CC1
|
847 |
-
O=C(N1CCN(c2ncnc3[nH]cc(Br)c23)CC1)C1(c2cccc(Br)c2)CCNCC1
|
848 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
849 |
-
Nc1ncnc2nc(-c3ccc(CN4CCC(c5cc(-c6ccncc6)[nH]n5)CC4)cc3)c(-c3ccccc3)cc12
|
850 |
-
O=C1CC2OC3(CCCC3)C3=C(C(=O)c4ccccc4C3=O)C2O1
|
851 |
-
Cc1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)c(C)n2n1
|
852 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNC4CCOCC4)c4ccc(Cl)cc4)CC3)c21
|
853 |
-
N#Cc1c[nH]c2ncnc(N3CC4(CCNCC4)c4ccccc43)c12
|
854 |
-
COc1nc(Br)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
855 |
-
CCc1c(C)[nH]c(CC(C)(C)C2C(=O)Nc3ccc(-c4cncc(OCC(N)Cc5c[nH]c6ccccc56)c4)cc32)c1C
|
856 |
-
CNc1nccc(-c2ccc(C(=O)NC(CN)Cc3ccc(Cl)cc3Cl)s2)n1
|
857 |
-
COc1ccc(S(=O)(=O)Nc2cc(-c3ccc4nc(NC(C)=O)sc4c3)ccn2)cc1
|
858 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccccc1
|
859 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CC2CCN2)CC1
|
860 |
-
CN(C)C(=O)N1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
861 |
-
CC(=O)NC1CCN(c2cccc(-c3ccc4nc(-c5cccnc5N)n(-c5ccc(C6(N)CCC6)cc5)c4n3)c2)CC1
|
862 |
-
CCOC(=O)c1c(C)nc(NNC(=O)c2cccc3c(=O)c4ccccc4[nH]c23)nc1-c1cccc(OC)c1
|
863 |
-
Cc1cc(O)cc2c1OC(C)(CCCC(C)C)CC2=O
|
864 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cccnc3-4)cc2)CCC1
|
865 |
-
COc1ccccc1NC(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
866 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1csc2ccccc12
|
867 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c1cccc2C(F)(F)F
|
868 |
-
NC1(c2ccc(-c3nn4c(-c5cccc(CO)c5)cnc4cc3-c3ccccc3)cc2)CCC1
|
869 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CCCN)cc21
|
870 |
-
NC1(c2ccc(-c3nc4c5cc(F)ccc5nn4c(NC4CC4)c3-c3ccccc3)cc2)CCC1
|
871 |
-
COc1cc(-c2cnc[nH]2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
872 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CN)c4ccc(C(F)(F)F)cc4)CC3)c21
|
873 |
-
Cn1ncc(Br)c1-c1coc(C(=O)NC2(c3ccc(Cl)c(Cl)c3)CCNCC2)c1
|
874 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCCCCN)cc21
|
875 |
-
Cl.NCc1ccc(-n2c(-c3cccnc3N)nc3ccc(-c4cn[nH]c4)nc32)cc1
|
876 |
-
NC1(C(=O)NC(CCCO)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
877 |
-
CNc1c(-c2ccccc2)c(-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)nc2nccn12
|
878 |
-
CCn1c(-c2nonc2N)nc2cncc(C(=O)N3CCC(N)C3)c21
|
879 |
-
NC(=O)c1ccc(NC2CNCCC2c2ccc(F)c(Cl)c2)c2cncnc12
|
880 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCO)CC1
|
881 |
-
Nc1cc2[nH]nc(Cl)c2cc1-c1cncc(OCC(N)Cc2c[nH]c3ccccc23)c1
|
882 |
-
NC1(C(=O)NC(CCO)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
883 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(C(=O)NCCC(N)=O)CC4)cc3)nc2n1
|
884 |
-
NC(CNc1nnc(-c2ccc3c(c2)CC(=O)N3)s1)Cc1ccc(C(F)(F)F)cc1
|
885 |
-
O=C(Cc1nc(N2CCOC(CO)C2)cc(=O)[nH]1)N1CCc2c(Cl)cccc21
|
886 |
-
CC(C)Cc1nc(-c2ccc(CN3CCC(n4c(O)nc5ccccc54)CC3)cc2)c(-c2ccccc2)nc1O
|
887 |
-
[O-][n+]1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6nc(N7CCN(CCO)CC7)ncc6cc5-c5ccccc5)cc4)CC3)[nH]2)cc1
|
888 |
-
Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccc(F)cc4)c3)cc2o1
|
889 |
-
O=c1[nH]c2ccccc2n1C1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
890 |
-
COC(=O)c1cc(OC)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
891 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(C(F)(F)F)cc4)cn3CCN3CCCC3)CC2)c1C1CCC1
|
892 |
-
Nc1cn2nc(-c3cnc(Cl)c(NS(=O)(=O)c4ccc(F)cc4)c3)ccc2n1
|
893 |
-
NCC(Cc1ccccc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
894 |
-
Cc1cc(CC(N)COc2cncc(-c3ccc4[nH]nc(C)c4c3)c2)ccc1F
|
895 |
-
OCc1cnn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc12
|
896 |
-
CC(=O)N1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
897 |
-
COc1ccc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)cc1F
|
898 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccccc4Cl)c3)cc12
|
899 |
-
COc1ccc(-c2cc3c(C)nc(N)nc3n(C3CCC(OCC(N)=O)CC3)c2=O)cn1
|
900 |
-
c1ccc(-c2cc3cnc(N4CCn5cnnc5C4)nc3nc2-c2ccc(CN3CCC(c4nnc(-c5ccccn5)[nH]4)CC3)cc2)cc1
|
901 |
-
COc1cc(OC)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
902 |
-
N#Cc1ccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)cc1
|
903 |
-
CN(C)CCNc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(c5nnc(-c6ccccn6)[nH]5)CC4)cc3)nc2n1
|
904 |
-
CCCC1OC(CC(=O)O)CC2=C1C(=O)c1c(O)cccc1C2=O
|
905 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)n1C
|
906 |
-
COC(=O)c1cc(OC)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
907 |
-
c1ccc(-c2cc3c(ccn4cnnc34)nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
908 |
-
Cc1nc(-c2ccccc2)c(-c2ccc(CN3CCC(n4c(=O)[nH]c5ccccc54)CC3)cc2)[nH]c1=O
|
909 |
-
CNc1nccc(-c2ccc(C(=O)NC(Cc3ccc(Cl)cc3Cl)CN(C)C)s2)n1
|
910 |
-
CS(=O)(=O)c1ccc(-c2ccc3c(c2)nn2cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc32)cc1
|
911 |
-
CCn1c(-c2nonc2N)nc2cncc(CNC3CCNCC3)c21
|
912 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4cccc(C(F)(F)F)c4)c3)cc12
|
913 |
-
CC1COCCN1c1nc(N2CCOCC2C)c2ccc(-c3cccc(N)c3)nc2n1
|
914 |
-
COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4
|
915 |
-
NC1(c2ccc(-c3nc4nc(O)ccn4c3-c3ccccc3)cc2)CCC1
|
916 |
-
c1ccc(-c2cc3cccnc3nc2-c2ccc(CN3CCC(c4cc(-c5cccnc5)[nH]n4)CC3)cc2)cc1
|
917 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cc(F)c(F)c(F)c1
|
918 |
-
NC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3[nH]cc(Cl)c23)CC1
|
919 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)c(Cl)c(=O)[nH]1
|
920 |
-
Cn1cc(C(CN)Oc2cncc(C=Cc3ccncc3)c2)c2ccccc21
|
921 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CNC)cc21
|
922 |
-
Cn1nc(N)c2cc(-c3ccccc3)c(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)nc21
|
923 |
-
CC(C)NC(=O)c1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
924 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3F)CC1)c1ccc(Cl)cc1
|
925 |
-
CC1(C#N)CN(c2cnc(C(=O)Nc3csc(-c4nncn4C4CC4)n3)cc2-n2cnc(C3CC3)c2)C1
|
926 |
-
COc1ccc2c(c1)-c1nc(-c3ccc(C4(N)CC(C)(O)C4)cc3)c(-c3ccccc3)n1CO2
|
927 |
-
Cc1cccc(NC(=O)NCc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)c1
|
928 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2cccc(F)c2)sc1Cl
|
929 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3c(F)cccc3-4)cc2)CC(O)(C2CC2)C1
|
930 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)cc4)cn3CCN3CCCC3)CC2)c1Br
|
931 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4cccc(F)c4)c3)cc12
|
932 |
-
c1ccc(-c2cn3ccnc3nc2-c2ccc(CN3CCC(c4cnc5ccccc5n4)CC3)cc2)cc1
|
933 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCO)CC2c2ccc(F)c(F)c2)oc1Cl
|
934 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccc(-c5cn[nH]c5)cc3-4)cc2)C1
|
935 |
-
c1ccc(-c2cc3cccnc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
936 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2Br)CC1
|
937 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4cccc(C(C)(C)C)c4)c3)cc2s1
|
938 |
-
Cn1ncc(Br)c1-c1oc(C(=O)NC2CNCCC2c2ccc(Cl)cc2)cc1Br
|
939 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(Cl)cc3)cn2CCN2CCCC2)CC1
|
940 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(Br)cc4)c3)cc12
|
941 |
-
COc1cc2ncc3c(N)nc(-c4cncc(OCC(N)Cc5cccc(Cl)c5)c4)cc3c2cc1OC
|
942 |
-
N#Cc1cc2cc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)ccc2cn1
|
943 |
-
NC1(c2ccc(-c3nn4c(-c5ccn[nH]5)cnc4cc3-c3ccccc3)cc2)CCC1
|
944 |
-
COC(=O)c1cccc(-c2ccc3nn4cc(-c5ccccc5)c(-c5ccc(C6(N)CCC6)cc5)nc4c3c2)c1
|
945 |
-
NC1(c2ccc(-c3nc4ccc(-c5cc[nH]n5)cn4c3-c3ccccc3)cc2)CCC1
|
946 |
-
Sc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
947 |
-
NC(=O)C=Cc1ccc2nn3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc3c2c1
|
948 |
-
Nc1ncccc1-c1nc2cc(Br)cnc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
949 |
-
NC1(Cc2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
950 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCC3)CC2)c1-c1ccc(F)c(F)c1
|
951 |
-
Cn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C=O)CC1
|
952 |
-
CC1CN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CCN1
|
953 |
-
CCC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cccnc3-4)cc2)C1
|
954 |
-
Cn1nccc1-c1csc(C(=O)NC2(c3ccc(F)cc3)CCNCC2)c1
|
955 |
-
Cc1n[nH]c2ncc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)cc12
|
956 |
-
CC(=O)OC(C)(C)C(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(C5(N)CCC5)cc4)c3n2)c1
|
957 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccccc4F)c3)cc2s1
|
958 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(-c3ccccc3Cl)cccc21
|
959 |
-
Nc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
960 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)c(C(F)(F)F)c1
|
961 |
-
Cc1cc(-c2cn(CCNCC3CC3)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)ccc1F
|
962 |
-
O=C(NC(c1ccc(Cl)c(Cl)c1)C1CCNCC1)c1ccc2cnc(Cl)cc2c1
|
963 |
-
CC(C)=C1C(=O)Nc2ccc(NC(COc3cncc(-c4ccc5c(c4)C(=C(C)C)C(=O)N5)c3)Cc3c[nH]c4ccccc34)cc21
|
964 |
-
CN(C)CCC1CC(c2ccc(Cl)c(Cl)c2)C(NC(=O)c2cc(-c3c(Cl)cnn3C)c(Cl)o2)CN1
|
965 |
-
Cn1nccc1-c1coc(C(=O)NC2CNCCC2c2ccccc2)c1.O=C(O)C(O)C(O)C(=O)O
|
966 |
-
NC1(c2ccc(-c3nc4c(Br)cccn4c3-c3ccccc3)cc2)CCC1
|
967 |
-
CC(C)N(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
968 |
-
NCC(Cc1ccc(C(F)(F)F)cc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
969 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cc(F)c(F)cc1F
|
970 |
-
Cc1nc2cnc3ccc(C#Cc4cccnc4)cc3c2n1-c1ccc(CC#N)cc1
|
971 |
-
NC1(c2ccc(-c3ncc4cnccc4c3-c3ccccc3)cc2)CCC1
|
972 |
-
Cc1c(-c2ccn[nH]2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1C
|
973 |
-
NC(=O)c1cc(Br)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
974 |
-
O=c1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6cc[nH]c(=O)c6cc5-c5ccccc5)cc4)CC3)[nH]2)c[nH]1
|
975 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C)c3)cn2CCN(CC)C(C)C)CC1
|
976 |
-
CC(C)c1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4Cl)CC3)n2)ccn1
|
977 |
-
NC1(c2ccc(-c3ncc4cccn4c3-c3ccccc3)cc2)CCC1
|
978 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2n1
|
979 |
-
CC(=O)Nc1ccc(S(=O)(=O)NCc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)cc1
|
980 |
-
CCCOc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2n1
|
981 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2ccc(Cl)cc2)n(C)c1Cl
|
982 |
-
NC1(c2ccc(-c3nc4cc(-c5ncc[nH]5)ccn4c3-c3ccccc3)cc2)CCC1
|
983 |
-
COc1cccc(CNC(=O)NC2CCN(Cc3ccc(-c4nnc5n4-c4cccnc4Nc4ccccc4-5)cc3)CC2)c1
|
984 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc(Br)c1O
|
985 |
-
O=C(NC(c1ccc(Cl)c(-c2ccccc2)c1)C1CCNCC1)c1ccc2cnccc2c1
|
986 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4c(F)cc(F)cc4Br)c3)cc12
|
987 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CC(C)C2)CC1
|
988 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(-c7ccccn7)nn6c(NC(C)C)c5-c5ccccc5)cc4)C3)n[nH]2)n1
|
989 |
-
NC1(Cc2ccc(Cl)c(Cl)c2)CCN(c2ncnc3[nH]ccc23)CC1
|
990 |
-
CC(C)(C)c1ccc(CC2(N)CCN(c3ccnc4[nH]ccc34)CC2)cc1
|
991 |
-
NC(=O)c1ccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)cc1
|
992 |
-
Cc1cccc(-c2nc(C3CCN(Cc4ccc(-c5nc6nccn6cc5-c5ccc(F)cc5)cc4)CC3)n[nH]2)n1
|
993 |
-
COC(=O)c1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
994 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccc(F)cc4)c3)cc2[nH]1
|
995 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CN4CCC(n5ncc6c(N)ncnc65)CC4)cc3)nc2n1
|
996 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccccc4)c3)cc2s1
|
997 |
-
Nc1nc(N)c2cc(-c3ccccc3)c(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)nc2n1
|
998 |
-
Cc1cc(-c2cn(CCN3CCCC3)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)ccc1F
|
999 |
-
Cc1ccc(CC(CNC(C)C)C(=O)N2CCN(c3ncnc4c3C(C)SC4)CC2)cc1
|
1000 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CCCN)cc21
|
1001 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3)CC1)c1ccc(Cl)cc1
|
1002 |
-
COc1cccc2c1CCN2C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
1003 |
-
COCC(NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(C(C)C)N(C)C(=O)C(CCCNC(=N)N)NC(=O)C1CCCN1C(=O)C(N)CCCNC(=N)N)C(=O)NCCCC(=O)O
|
1004 |
-
CCOc1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1005 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCCN)cc21
|
1006 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc2sccc12
|
1007 |
-
Cn1cncc1-c1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1008 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1-c1cn[nH]c1
|
1009 |
-
O=C(NC(c1ccc(Cl)c(Cl)c1)C1CCCNC1)c1ccc2cnccc2c1
|
1010 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)C(CN)NC(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(N)=O
|
1011 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
1012 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)Cc3ccccc3)cc21
|
1013 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CN)c4ccc(Cl)cc4)CC3)c21
|
1014 |
-
O=C1C(=Cc2ccccn2)CNCC1=Cc1ccccn1
|
1015 |
-
Nn1c(CC(=O)N2CCc3ccccc32)nc(N2CCOCC2)cc1=O
|
1016 |
-
CCCC1OC2CC(=O)OC2C2=C1C(=O)c1ccccc1C2=O
|
1017 |
-
CN1CCN(c2ccc3nc(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)c(-c4ccccc4)nc3n2)CC1
|
1018 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cc(C(N)=O)ccc3-4)cc2)C1
|
1019 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4ccc(Cl)cc4)c3)cc2s1
|
1020 |
-
CCC(C)(C)NCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
1021 |
-
COc1ccc2c(c1)OCn1c-2nc(-c2ccc(C3(N)CC(O)(C4CC4)C3)cc2)c1-c1ccccc1
|
1022 |
-
CCC1SCc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c21
|
1023 |
-
OCCN1CCN(c2ccc3nc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ncccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
1024 |
-
O=C1CC2OC(COCc3ccccc3)C3=C(C(=O)c4ccccc4C3=O)C2O1
|
1025 |
-
CCOc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1026 |
-
CC1COCCN1c1nc(N2CCOCC2)nc2nc(-c3ccc(N)nc3)ccc12
|
1027 |
-
NC(=O)Nc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1028 |
-
NC(=O)C(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
1029 |
-
CCOc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN(C)C)CC1
|
1030 |
-
NC(=O)Nc1ccc2c(c1)C(=Cc1cc(-c3cccc(C(=O)NCCN4CCCCC4)c3)c[nH]1)C(=O)N2
|
1031 |
-
NC(=O)c1cc(Cl)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1032 |
-
Cc1cc(-c2cn(CC3CNC3)c(C3CCC(c4ncnc(N)c4C(C)C)CC3)n2)ccc1F
|
1033 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(C)cn6cc5-c5ccccc5)cc4)C3)n[nH]2)n1
|
1034 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccc(-c5cn[nH]c5)cc3-4)cc2)C1
|
1035 |
-
NC(Cc1ccccc1)c1ccc(-c2ncnc3[nH]cnc23)cc1
|
1036 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1ccccc1
|
1037 |
-
N#Cc1cc(-c2ccccc2)c(-c2ccc(CN3CCC(n4c(=O)[nH]c5ccccc54)CC3)cc2)nc1Cl
|
1038 |
-
N=c1ccc2nc(-c3ccc(C4(NC(=O)Cc5cccnc5)CCC4)cc3)c(-c3ccccc3)cc2n1C(N)=O
|
1039 |
-
NC(COc1cncc(-c2ccc3c(c2)C(c2ccco2)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
1040 |
-
CNc1nccc(-c2ccc(C(=O)NC(CN)Cc3ccc(Cl)cc3Cl)s2)n1
|
1041 |
-
NC(CNc1ncc(-c2ccc3cncnc3c2)s1)Cc1ccc(C(F)(F)F)cc1
|
1042 |
-
NC1(c2ccc(-c3nc4cc(F)ccn4c3-c3ccccc3)cc2)CCC1
|
1043 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2C2CCNCC2)CC1
|
1044 |
-
COc1nn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CC(c5n[nH]c(-c6cccc(C)n6)n5)C4)cc3)nc2c1CO
|
1045 |
-
CC(=C1C(=O)Nc2ccc(NC(N)=O)cc21)c1cc(CN)c[nH]1
|
1046 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccccc4OC(F)(F)F)c3)cc12
|
1047 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C2=CCCC2)CC1
|
1048 |
-
O=C(NCCc1ccc(Cl)cc1)NC1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
1049 |
-
Cc1cc(-c2cn(CC3CN(CO)C3)c(C3CCN(c4ncnc(N)c4C(C)C)CC3)n2)ccc1F
|
1050 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CCN3CCCCC3)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
1051 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(F)cc(F)cc21
|
1052 |
-
N#Cc1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1053 |
-
Cc1nc(-c2ccoc2)c(-c2cnc3[nH]nc(C)c3n2)cc1OCC(N)Cc1c[nH]c2ccccc12
|
1054 |
-
CC1=NN(C(=O)c2ccc(N)cc2)C(=O)C1N=Nc1ccc(S(=O)(=O)Nc2ncccn2)cc1
|
1055 |
-
CC(O)c1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1056 |
-
CC(C)(O)C(=O)Nc1cccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(C5(N)CCC5)cc4)c3n2)c1
|
1057 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccnc(C(C)C)c3)cn2CCN2CCC2)CC1
|
1058 |
-
NCC(C(=O)N1CCN(c2ncnc3[nH]cc(Cl)c23)CC1)c1ccc(Cl)c(Cl)c1
|
1059 |
-
CC1Cc2c(Br)cccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)n1C
|
1060 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OC(CN)c3ccccc3)cc21
|
1061 |
-
CC(C)NC(c1ccc(Cl)cc1)c1ccc(-c2ccncc2)cc1
|
1062 |
-
CN1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccc(N)nc7)[nH]6)CC5)cc4)nc3n2)CC1
|
1063 |
-
CCCc1cc(-c2cncc(OCC(N)Cc3c[nH]c4ccccc34)c2)cc2c(C)n[nH]c12
|
1064 |
-
COc1cccc2c1-c1nc(-c3ccc(C4(N)CC(O)(C5CC5)C4)cc3)c(-c3ccccc3)n1CO2
|
1065 |
-
O=C1NCCc2nc(-c3ccc(CN4CCC(c5nnc(-c6ccccn6)[nH]5)CC4)cc3)c(-c3ccccc3)cc21
|
1066 |
-
COc1ccc(-c2nc3ccc(-c4cccc(N5CCOCC5)c4)nc3n2-c2ccc(C3(N)CCC3)cc2)cc1
|
1067 |
-
Nc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc12
|
1068 |
-
NC1(c2ccc(-c3nc4ccc(O)cn4c3-c3ccccc3)cc2)CCC1
|
1069 |
-
C=Cc1cnc2cc(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nn12
|
1070 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(Br)cccc21
|
1071 |
-
NC(=O)Nc1ccc2c(c1)C(=Cc1cc(-c3cccnc3)c[nH]1)C(=O)N2
|
1072 |
-
CNc1cc(C=Cc2cncc(OCC(N)Cc3c[nH]c4ccccc34)c2)ccn1
|
1073 |
-
COC(=O)c1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1074 |
-
NC1(c2ccc(-c3nc4ccc(-c5ccncc5)cn4c3-c3ccccc3)cc2)CCC1
|
1075 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3O)CC1)c1ccc(Cl)cc1
|
1076 |
-
CC(C)(N)c1ccc(-n2c(-c3cccnc3N)nc3ccc(-c4ccccc4)nc32)cc1
|
1077 |
-
NC1(Cc2ccccc2OC(F)(F)F)CCN(c2ncnc3[nH]ccc23)CC1
|
1078 |
-
O=C(Cc1nc(-c2ccncc2)cc(=O)[nH]1)N1CCc2c(F)cccc21
|
1079 |
-
Cl.Cn1ncc(C(=O)O)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)s1
|
1080 |
-
c1ccc(-c2cn3nc(C4CC4)nc3nc2-c2ccc(CN3CCC(c4cnc5ccccc5n4)CC3)cc2)cc1
|
1081 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccccc1F
|
1082 |
-
CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CC(C(F)F)C3)cn2)cs1)C(F)(F)F
|
1083 |
-
Nc1cc(N2CCC(c3nc(-c4cccc(F)c4)cn3CCN3CCCC3)CC2)ncn1
|
1084 |
-
Cc1cc(F)ccc1S(=O)(=O)NCC(O)CN1CCCC2(CCN(c3ncnc(N)c3C3CC3)C2)C1
|
1085 |
-
CS(=O)(=O)OCc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1
|
1086 |
-
NC1(C(=O)NC(CCCN2CCCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
1087 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1c2ccccc2CC1c1ccccc1
|
1088 |
-
CC(C)CC(=O)Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1
|
1089 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNCCF)c4ccc(Cl)cc4)CC3)c21
|
1090 |
-
Cc1nc(N)nc2c1cc(-c1cnc3ccccc3c1)c(=O)n2C1CCC(OCC(N)=O)CC1
|
1091 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(-c3ccccc3F)cccc21
|
1092 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)c3ccccc3)cc21
|
1093 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCC(=O)c4ccccc4N)cc3)nc2n1
|
1094 |
-
NC1(c2ccc(-c3nc4cc(-n5cccn5)ccn4c3-c3ccccc3)cc2)CCC1
|
1095 |
-
Clc1ccc2nc(CCNCc3ccc(-c4nnc5n4-c4cccnc4Nc4ccccc4-5)cc3)[nH]c2c1
|
1096 |
-
O=C(N1CCN(c2ncnc3[nH]ccc23)CC1)C1(c2ccc(Cl)cc2)CCNCC1
|
1097 |
-
CC1Cc2c(O)cccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
1098 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CN(C)C)cc21
|
1099 |
-
Cc1c(NCCN(C)C)cc(Cl)cc1N1CCN(c2ncnc3[nH]nc(Br)c23)CC1
|
1100 |
-
NC(=O)Nc1ccc2c(c1)C(=Cc1cc(-c3cccc(C(N)=O)c3)c[nH]1)C(=O)N2
|
1101 |
-
NC1(c2ccc(-c3nc4c5cccc(-c6cn[nH]c6)c5nn4cc3-c3ccccc3)cc2)CCC1
|
1102 |
-
CC1Cc2c(ccc(F)c2F)N1C(=O)Cc1nc(N2CCOCC2)cc(=O)n1C
|
1103 |
-
O=C(NCc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1cccnc1
|
1104 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(C(F)(F)F)c(F)c3)cn2CCN2CCC(F)(F)CC2)CC1
|
1105 |
-
Oc1nc2cc(NC(COc3cncc(-c4ccc5[nH]c(O)nc5c4)c3)Cc3c[nH]c4ccccc34)ccc2[nH]1
|
1106 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1c2ccccc2CC1C1CC1
|
1107 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCc4n[nH]c(-c5ccccc5)n4)cc3)nc2n1
|
1108 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNC(C)(C)CO)c4ccc(Cl)cc4)CC3)c21
|
1109 |
-
CCc1cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c(OC)n1
|
1110 |
-
CC(=C1C(=O)Nc2ccc(NC(N)=O)cc21)c1cc(CNC(=O)C2CNCCN2)c[nH]1
|
1111 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccncc3)COc3cccc(F)c3-4)cc2)CC(O)(C2CC2)C1
|
1112 |
-
CCN(CC)CCNC(=O)c1ccc2nc(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)c(-c3ccccc3)nc2c1
|
1113 |
-
Cn1c(CC(=O)Nc2cccc3sccc23)nc(N2CCOCC2)cc1=O
|
1114 |
-
CN(C)C1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
1115 |
-
Oc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
1116 |
-
CC(C)c1ccc(CC(CN)C(=O)N2CCN(c3ncnc4[nH]ccc34)CC2)cc1
|
1117 |
-
COc1ccc(CC(CN)NC(=O)c2cc(Br)c(-c3ccnc4[nH]ccc34)s2)cc1
|
1118 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCCCNCCc3ccc(OC)cc3)c21
|
1119 |
-
Cc1c[nH]c2ncnc(N3CCC(N)C3)c12
|
1120 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3ccsc3)cc12
|
1121 |
-
Nc1ncnc2nc(-c3ccc(CN4CCC(n5cnc6c(N)ncnc65)CC4)cc3)c(-c3ccccc3)cc12
|
1122 |
-
Nc1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nc6nc(N7CCN(CCO)CC7)ccc6nc5-c5ccccc5)cc4)CC3)[nH]2)cn1
|
1123 |
-
Nc1ncccc1-c1nc2cc(-c3cccnc3)cnc2n1-c1ccc(CNC(=O)c2cccc(F)c2)cc1
|
1124 |
-
Cn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2-c2cccc(C#N)c2)CC1
|
1125 |
-
NC1(c2ccc(-c3nc4cc(-c5cnc[nH]5)ccn4c3-c3ccccc3)cc2)CCC1
|
1126 |
-
COC(=O)c1cnn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CCC(c5n[nH]c(-c6ccccn6)n5)CC4)cc3)nc12
|
1127 |
-
NC1(c2ccc(-c3nc4ncc(-c5ccccc5)cn4c3-c3ccccc3)cc2)CCC1
|
1128 |
-
CC(C)(Cc1ccco1)C1C(=O)Nc2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc21
|
1129 |
-
NC1(C(=O)NCc2ccc(OC(F)(F)F)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
1130 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cnccc3-4)cc2)CCC1
|
1131 |
-
Nc1ncccc1-c1nc2cccnc2n1-c1ccc(CNC(=O)C2CCCCC2)cc1
|
1132 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1ccc2ccccc2c1
|
1133 |
-
NC1(c2ccc(-c3nc4nc(C5CC5)ccn4c3-c3ccccc3)cc2)CCC1
|
1134 |
-
COc1cc(Cl)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1135 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(Br)c(F)c4)c3)cc12
|
1136 |
-
Cc1cc(-c2cn(CC3CNC3)c(C3CCN(c4ncnc(N)c4OC(C)C)CC3)n2)ccc1F
|
1137 |
-
CSc1ncc2cc(-c3ccccc3)c(-c3ccc(CNCCC(N)=O)cc3)nc2n1
|
1138 |
-
Cc1cccc(C)c1S(=O)(=O)N1CCCC1C(O)CN1CCCC2(CCN(c3ncnc(N)c3C3CC3)C2)C1
|
1139 |
-
Cc1ccc2[nH]c(C3CCN(Cc4ccc(-c5ncc(-c6nn[nH]n6)cc5-c5ccccc5)cc4)CC3)nc2c1
|
1140 |
-
CC1Cc2c(F)cccc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)[nH]1
|
1141 |
-
CC(C)(C)NC(c1ccc(Cl)cc1)c1ccc(-c2ccncc2)cc1
|
1142 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=S)n1C
|
1143 |
-
NC(CNc1cnc(-c2ccc3cnccc3c2)s1)Cc1ccc(C(F)(F)F)cc1
|
1144 |
-
O=C1N=CC=C2N=C(c3ccc(CN4CCC(c5n[nH]c(-c6ccccn6)n5)CC4)cc3)C(c3ccccc3)=CC12
|
1145 |
-
Cc1nc2nc(-c3ccc(CN4CCC(c5nc6ccc(C#N)cc6[nH]5)CC4)cc3)c(-c3ccccc3)cn2n1
|
1146 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3F)CC1)c1ccc(Cl)cc1
|
1147 |
-
NC1(c2ccc(-c3nc4ccc(C(=O)NCC5CC5)cn4c3-c3ccccc3)cc2)CCC1
|
1148 |
-
Brc1cnc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn12
|
1149 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccnc(C(F)(F)F)c3)cn2CCNC)CC1
|
1150 |
-
C#Cc1ncc(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1ccc2cnccc2c1
|
1151 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCC2)CC1
|
1152 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(F)c3)cn2CCN2CCCC2)CC1
|
1153 |
-
[C-]#[N+]COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1154 |
-
Cc1noc(-c2cc(NCCN3CCCC3)c(C)c(N3CCN(c4ncnc5[nH]nc(Br)c45)CC3)c2)n1
|
1155 |
-
CC(C)NCC(Cc1ccc(Cl)c(F)c1)C(=O)N1CCN(c2ncnc3c2C(C)OC3)CC1
|
1156 |
-
Fc1ccc2c(c1)C1(CCNCC1)CN2c1ncnc2[nH]ccc12
|
1157 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4cccc(C(F)(F)F)c4)s3)cc12
|
1158 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(C(C)C)nn6cc5-c5ccccc5)cc4)C3)n[nH]2)n1
|
1159 |
-
NC1(C(=O)NC(CCCN2CCOCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
1160 |
-
CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CC4CC4C3)cn2)cs1)C(F)(F)F
|
1161 |
-
CCN(CC)CCNC(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)nc2c1
|
1162 |
-
Cc1cc(-c2cn(CCO)c(C3CCN(c4ncnc(N)c4C(N)=O)CC3)n2)ccc1F
|
1163 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21
|
1164 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4csc5ccccc45)c3)cc12
|
1165 |
-
Cc1ccc(S(=O)(=O)NCC(O)CN2CCCC3(CCN(c4ncnc(N)c4C4CC4)C3)C2)c(C)c1
|
1166 |
-
NC1(C(=O)NC(c2ccc(Cl)cc2)C2CC2)CCN(c2ncnc3[nH]ccc23)CC1
|
1167 |
-
NC(CNC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
1168 |
-
CON(C)C(=O)c1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1169 |
-
c1ccc(-c2cn3nc(C4CC4)nc3nc2-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)cc1
|
1170 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
1171 |
-
N#Cc1ccc(CC(N)C(=O)N2CCN(c3ncnc4ccccc34)CC2)cc1
|
1172 |
-
O=C(NCCc1cccs1)NC1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
1173 |
-
COC(=O)c1c(C)nc(NNC(=O)c2cccc3c(=O)c4ccccc4[nH]c23)nc1-c1ccccc1
|
1174 |
-
COc1cc(-c2ccc3c(c2)nn2cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc32)ccc1F
|
1175 |
-
Cc1cc(-c2cncc(OCC(N)Cc3c[nH]c4ccccc34)c2)cc2ccncc12
|
1176 |
-
NCC(C(=O)Nc1ccc(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
1177 |
-
Cc1n[nH]c2ccc(-c3cncc(SCC(N)Cc4c[nH]c5ccccc45)c3)cc12
|
1178 |
-
NC1(CNC(=O)c2ccc(F)cc2F)CCN(c2ncnc3[nH]cc(Cl)c23)C1
|
1179 |
-
COc1nn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CCC(c5n[nH]c(-c6ccccn6)n5)CC4)cc3)nc2c1CO
|
1180 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(C(F)(F)F)c(F)c4)cn3CCN3CCCC3)CC2)c1C1CCC1
|
1181 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNCC4CC4)c4ccc(Cl)cc4)CC3)c21
|
1182 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNC4CCOCC4)c4ccc(Cl)cc4)CC3)c21
|
1183 |
-
Cn1c(CC(=O)N2CCc3ccc(F)cc32)nc(N2CCOCC2)cc1=O
|
1184 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2cc(F)c(F)cc21
|
1185 |
-
Nc1ncnc2c1cnn2C1CCN(Cc2ccc(-c3nc4ccnn4cc3-c3ccccc3)cc2)CC1
|
1186 |
-
NC1(c2ccc(-c3nc4ccc(-c5ccncc5)cn4c3-c3ccccc3)cc2)CCC1
|
1187 |
-
CNC(=O)c1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1188 |
-
Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
1189 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4c[nH]c5ccncc45)cnc3-c3ccoc3)cc12
|
1190 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1N
|
1191 |
-
CCn1c(-c2nonc2N)nc2cncc(OCC3CCNCC3)c21
|
1192 |
-
NC(=O)Nc1ccc2c(c1)C(=Cc1cc(C(=O)O)c[nH]1)C(=O)N2
|
1193 |
-
NCC(NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
1194 |
-
Cc1nc(-c2ccoc2)c(-c2cnc3[nH]nc(C)c3n2)cc1OCC(N)Cc1c[nH]c2ccccc12
|
1195 |
-
NC1(c2ccc(-c3nc4cc(-c5ccccn5)ccn4c3-c3ccccc3)cc2)CCC1
|
1196 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(F)c(CO)c5)cnc4cc3-c3ccccc3)cc2)CCC1
|
1197 |
-
CNC1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4
|
1198 |
-
CC(=N)n1c(=N)ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)cc21
|
1199 |
-
NC1(c2ccc(-c3nc4nc(-c5ccccc5)ccn4c3-c3ccccc3)cc2)CCC1
|
1200 |
-
NC1CCN(c2ncnc3[nH]ccc23)CC1
|
1201 |
-
NC1(c2ccc(-c3nc4cc(CO)ccn4c3-c3ccccc3)cc2)CCC1
|
1202 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(S(C)(=O)=O)cc1
|
1203 |
-
Cc1cc(-c2cn(CCN(C)C(C)C)c(C3CCN(c4ncnc(N)c4C#N)CC3)n2)ccc1F
|
1204 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CC(=O)NCC(O)CO)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
1205 |
-
NC(CNc1nnc(-c2ccc3[nH]nc(C4CC4)c3c2)s1)Cc1ccc(C(F)(F)F)cc1
|
1206 |
-
CNc1nccc(-c2ccc(C(=O)NC(CN)Cc3ccc(Cl)cc3Cl)s2)n1
|
1207 |
-
CC1Cc2c(Br)cccc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)[nH]1
|
1208 |
-
Cc1cc(F)ccc1CC(N)COc1cncc(-c2ccc3[nH]nc(C)c3c2)c1
|
1209 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCCCCN)cc21
|
1210 |
-
Cc1ccc(C(=O)Nc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)cc1
|
1211 |
-
Cc1n[nH]c2cnc(-c3cncc(OCC(N)Cc4cccc(F)c4F)c3)cc12
|
1212 |
-
CCN(CC)C(=O)C1CCN(c2cccc(-c3ccc4nc(-c5cccnc5N)n(-c5ccc(C6(N)CCC6)cc5)c4n3)c2)CC1
|
1213 |
-
NCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
1214 |
-
O=C(NC(c1ccc(Cl)c(Cl)c1)C1CNC1)c1ccc2cnccc2c1
|
1215 |
-
Nc1ncccc1-c1nc2cccnc2n1-c1ccc(CNCc2ccccc2)cc1
|
1216 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccc(-c5ccn[nH]5)cc3-4)cc2)C1
|
1217 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(c4ccc(Cl)cc4)C4CCCN4)CC3)c21
|
1218 |
-
CNCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
1219 |
-
CN(C)CCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
1220 |
-
NC(COc1cncc(-c2ccc3[nH]nc(C(F)(F)F)c3c2)c1)Cc1c[nH]c2ccccc12
|
1221 |
-
c1ccc(-c2cc3cnc(N4CCOCC4)nc3nc2-c2ccc(CN3CCC(c4nnc(-c5ccccn5)[nH]4)CC3)cc2)cc1
|
1222 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(-c3ccccn3)cccc21
|
1223 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccccc3-4)cc2)C1
|
1224 |
-
Cn1nnnc1-c1cnc(-c2ccc(CN3CCC(n4c(=O)[nH]c5ccccc54)CC3)cc2)c(-c2ccccc2)c1
|
1225 |
-
CC1SCc2ncnc(N3CCN(C(=O)C(Cc4ccc(Cl)c(F)c4)CC4(N)CC4)CC3)c21
|
1226 |
-
COc1ccc(CC(CN)NC(=O)c2cc(Br)c(-c3ccnc4[nH]ccc34)s2)cc1
|
1227 |
-
Nc1ncccc1-c1nc2cc(-c3ccccc3)cnc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
1228 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2cccc(F)c2)oc1Cl
|
1229 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2C2CCNCC2)CC1
|
1230 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(S(N)(=O)=O)cc5)cnc4cc3-c3ccccc3)cc2)CCC1
|
1231 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCCN)cc21
|
1232 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(Cl)cc4)cn3CCN3CCCC3)CC2)c1C1CCC1
|
1233 |
-
NCC(CC1CCCCC1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
1234 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(F)cc5)c(Cl)nc4cc3-c3ccccc3)cc2)CCC1
|
1235 |
-
Cc1nc2nc(-c3ccc(CN4CCC(c5nc6cccnc6[nH]5)CC4)cc3)c(-c3ccccc3)cn2n1
|
1236 |
-
CC1CC(O)c2ncnc(N3CCN(C(=O)C(CNCC4CC4)c4ccc(C(F)(F)F)c(F)c4)CC3)c21
|
1237 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4cccc(Cl)c4)s3)cc12
|
1238 |
-
NC(Cc1ccccc1)C(=O)N1CCN(c2ncnc3ccccc23)CC1
|
1239 |
-
NC1(c2ccc(-c3nc4ccc(C(=O)NC5CC5)cn4c3-c3ccccc3)cc2)CCC1
|
1240 |
-
Fc1ccc(-c2cn3c(Cl)cnc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
1241 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(F)cc4F)c3)cc12
|
1242 |
-
NC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3ccccc23)CC1
|
1243 |
-
CCC(=O)N1CCN(c2cccc(-c3ccc4nc(-c5cccnc5N)n(-c5ccc(C6(N)CCC6)cc5)c4n3)c2)CC1
|
1244 |
-
NCC(O)(c1ccc(Cl)cc1)c1ccc(-c2cn[nH]c2)cc1
|
1245 |
-
Nc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1246 |
-
CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CCN(C4CC4)CC3)cn2)cs1)C(F)(F)F
|
1247 |
-
Cc1c(NCCN2CCCC2)cc(C(=O)CCC(F)(F)F)cc1N1CCN(c2ncnc3[nH]nc(Br)c23)CC1
|
1248 |
-
CC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)c(Br)c(=O)[nH]1
|
1249 |
-
NC1(c2ccc(-c3nc4ccc(F)cn4c3-c3ccccc3)cc2)CCC1
|
1250 |
-
COc1cc(-c2cnc[nH]2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1251 |
-
NC1(c2ccc(-c3nc4ccc(Cl)cn4c3-c3ccccc3)cc2)CCC1
|
1252 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cc(F)ccc3-4)cc2)C1
|
1253 |
-
Cl.Cn1nccc1-c1ccc(C(=O)NC2CNCCC2c2ccc(Cl)cc2)s1
|
1254 |
-
NC1(c2ccc(-c3nc4c(-c5ccc(F)cc5)cccn4c3-c3ccccc3)cc2)CCC1
|
1255 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccsc3)COc3cccc(F)c3-4)cc2)C1
|
1256 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)cc4)cn3CCN3CCCC3)CC2)c1Cl
|
1257 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCC3)CC2)c1-c1ccc(C(=O)O)cc1
|
1258 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3ccoc3)cc12
|
1259 |
-
COc1ccc(-c2cc3c(C)nc(N)nc3n(C3CCC(OCC(N)=O)CC3)c2=O)cn1
|
1260 |
-
Cl.NCc1ccc(-n2c(-c3cccnc3N)nc3ccc(-c4ccccc4)nc32)cc1
|
1261 |
-
CC1Cc2cc(F)c(F)cc2N1C(=O)Cc1nc(N2CCOCC2)c(F)c(=O)n1C
|
1262 |
-
NC1(c2ccc(-n3c(-c4ccc(Cl)cc4)nc4ccc(-c5cccc(N6CCOCC6)c5)nc43)cc2)CCC1
|
1263 |
-
CC(C)NCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
1264 |
-
CNC(=O)C1CCN(c2nc(N3CCOCC3C)c3ccc(-c4ccc(N)nc4)nc3n2)CC1
|
1265 |
-
COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1266 |
-
Cn1nccc1-c1coc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)c1.O=C(O)C(O)C(O)C(=O)O
|
1267 |
-
c1ccc(-c2nnc(C3CCN(Cc4ccc(-c5nnc6n5-c5ccccc5Nc5ccccc5-6)cc4)CC3)[nH]2)nc1
|
1268 |
-
NCc1cccc(-c2c[nH]c(C=C3C(=O)Nc4ccc(NC(N)=O)cc43)c2)c1
|
1269 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2ccccc2)cc1-c1cnc2[nH]nc(C)c2n1
|
1270 |
-
COC(=O)c1cnn2cc(-c3ccc(F)cc3F)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc12
|
1271 |
-
NC1(c2ccc(-c3nc4c(C5CC5)cccn4c3-c3ccccc3)cc2)CCC1
|
1272 |
-
Clc1ccc(C(NC2CCCCC2)c2ccc(-c3ncnc4[nH]cnc34)cc2)cc1
|
1273 |
-
CC(C)Oc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCC2)CC1
|
1274 |
-
Cn1c(CC(=O)N2CCc3c2cccc3C(F)(F)F)nc(N2CCOCC2)cc1=O
|
1275 |
-
Cc1ncc(-c2c(N)ncnc2N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3C)CC2)s1
|
1276 |
-
CC(=O)Nc1ccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CN)cc4)c3n2)cc1.Cl
|
1277 |
-
NC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3cc(F)ccc23)CC1
|
1278 |
-
NC(COc1cc(C=Cc2ccncc2)cnc1Cl)Cc1c[nH]c2ccccc12
|
1279 |
-
Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(CC(=O)Nc2ccccc2)cc1
|
1280 |
-
Cn1c(CC(=O)Nc2ccc(F)c(F)c2)nc(N2CCOCC2)cc1=O
|
1281 |
-
CS(=O)(=O)N1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
1282 |
-
COc1cccc2c1CCN2C(=O)Cc1nc(N2CCOCC2)cc(=O)n1C
|
1283 |
-
NC1(C(=O)NC(CO)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
1284 |
-
CCNC(=O)c1ccc2c(c1)-c1nc(-c3ccc(C4(N)CC(C)(O)C4)cc3)c(-c3ccccc3)n1CO2
|
1285 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3O)CC1)c1ccc(Cl)cc1
|
1286 |
-
CC(C)n1cc(C(=O)c2cncc(NC3CNCC3c3ccc(F)cc3)n2)c2c(N)ncnc21
|
1287 |
-
CC1CN(c2cccc(-c3ccc4nc(-c5cccnc5N)n(-c5ccc(C6(N)CCC6)cc5)c4n3)c2)CC(C)O1
|
1288 |
-
NCC(Cc1ccc(F)cc1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
1289 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CCN3CCCC3)CC2)c1C(=O)O
|
1290 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccnc(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1OCC(F)(F)F
|
1291 |
-
CC(=O)Nc1ccc(-c2ccc3nc(-c4cccnc4N)n(-c4ccc(CN)cc4)c3n2)cc1.Cl
|
1292 |
-
COCc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1293 |
-
O=S(=O)(NCCNCC=Cc1ccc(Br)cc1)c1cccc2cnccc12
|
1294 |
-
NCC(CC1CCCCC1)NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1
|
1295 |
-
CNS(=O)(=O)c1ccc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c2c1
|
1296 |
-
CC(=O)OCC1OC(NC(=O)CCCn2[se]c3ccccc3c2=O)C(OC(C)=O)C(OC(C)=O)C1OC(C)=O
|
1297 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2ccccc2)cc1-c1cnc2[nH]nc(C)c2n1
|
1298 |
-
C=CCC1CC(c2ccc(Cl)c(Cl)c2)C(NC(=O)c2cc(-c3c(Cl)cnn3C)c(Cl)o2)CN1
|
1299 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)Cc3ccccc3)cc21
|
1300 |
-
Nc1ncccc1-c1nc2cccnc2n1-c1ccc(CC(=O)Nc2ccccc2)cc1
|
1301 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C)c3)cn2CCN(CC)CC)CC1
|
1302 |
-
CCN(CC)CCn1cc(-c2ccc(F)c(C)c2)nc1C1CCN(c2ncnc(N)c2C#N)CC1
|
1303 |
-
CN(C)CCCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
1304 |
-
CN(C)C(=O)COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1305 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)Cc3ccccc3)cc21
|
1306 |
-
O=C(Nc1csc(-c2nncn2C2CC2)n1)c1cc(-n2cnc(C3CC3)c2)c(N2CCC3(CC2)COC3)cn1
|
1307 |
-
CN(C)c1n[nH]c2ccc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)cc12
|
1308 |
-
N#Cc1cc(-c2ccccc2)c(-c2ccc(CN3CCC(n4c(=O)[nH]c5ccccc54)CC3)cc2)nc1N
|
1309 |
-
COCCNCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3O)CC1)c1ccc(Cl)cc1
|
1310 |
-
CCOC1CN(c2cc(N)ncn2)CCC1c1nc(-c2ccc(F)c(C)c2)cn1CCN(CC)C(C)C
|
1311 |
-
NC1(c2ccc(-c3nn4c(-c5ccc(CO)cc5)cnc4cc3-c3ccccc3)cc2)CCC1
|
1312 |
-
CC(C)C(=O)N1CCN(c2cccc(-c3ccc4nc(-c5cccnc5N)n(-c5ccc(C6(N)CCC6)cc5)c4n3)c2)CC1
|
1313 |
-
CC(C)C1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
1314 |
-
NCC(c1ccc(Cl)cc1)c1ccc(-c2cn[nH]c2)cc1
|
1315 |
-
COC(=O)c1c(C)nc(NNC(=O)c2cccc3c(=O)c4ccccc4[nH]c23)nc1-c1ccccc1F
|
1316 |
-
CC(C)c1nc2nc(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)c(-c3ccccc3)cn2n1
|
1317 |
-
Cc1n[nH]c2ccc(-c3nnc(NCC(N)Cc4ccccc4C(F)(F)F)s3)cc12
|
1318 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(S(C)(=O)=O)cc1
|
1319 |
-
CC(=O)Nc1cn2cc(-c3cnc(Cl)c(NS(=O)(=O)c4ccc(F)cc4)c3)ccc2n1
|
1320 |
-
COCC1Cc2ccccc2N1C(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
1321 |
-
CC1CN(c2cc(=O)[nH]c(CC(=O)N3c4ccccc4CC3C)n2)CCO1
|
1322 |
-
COc1cc(CC(N)C(=O)N2CCN(c3ncnc4ccccc34)CC2)cc(OC)c1
|
1323 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(C)N)cc21
|
1324 |
-
Cc1cc(C)c(CC(N)COc2cncc(-c3cc4c(C)n[nH]c4cn3)c2)c(C)c1
|
1325 |
-
O=S(=O)(Nc1cc(-c2ccc3nccn3n2)cnc1Cl)c1ccc(F)cc1
|
1326 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)Cl)C3)c12
|
1327 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CN(CCCCCCN)C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(=O)N(CCCCN)CC(N)=O
|
1328 |
-
Nc1nccnc1-c1nc2ccc(-c3ccccc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
1329 |
-
COc1cc2c(cc1Nc1nc(Nc3cccc4c3C(=O)NC4)c3cc[nH]c3n1)N(C(=O)CN(C)C)CC2
|
1330 |
-
CCOC(=O)c1cc(Br)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1331 |
-
Cc1c[nH]c2ncnc(N3CCC(NS(=O)(=O)c4ccccc4)C3)c12
|
1332 |
-
COc1cc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)ccc1F
|
1333 |
-
O=C(N1CCN(c2ncnc3[nH]cc(Cl)c23)CC1)C1(c2ccc(Br)cc2)CCNCC1
|
1334 |
-
Cc1nc(N)nc2c1cc(-c1cnn(C)c1)c(=O)n2C1CCC(OCC(N)=O)CC1
|
1335 |
-
OC1CCN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CC1
|
1336 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2c(-c3cccnc3)cccc21
|
1337 |
-
CCn1c(-c2nonc2N)nc2c(-c3ccc[nH]3)ncc(OCCCN)c21
|
1338 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc(I)c1
|
1339 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccc(F)cc3-4)cc2)C1
|
1340 |
-
CS(=O)(=O)Nc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1341 |
-
COCCNCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3O)CC1)c1ccc(Cl)cc1
|
1342 |
-
N#Cc1cc(C(NC(=O)c2ccc3cnccc3c2)C2CCNCC2)ccc1Cl
|
1343 |
-
Cc1c(-c2ccn[nH]2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1C
|
1344 |
-
[C-]#[N+]COc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1345 |
-
CCCC1NC(=O)C(CCCNC(=N)N)NC(=O)CN(C(=O)C(N)CCCNC(=N)N)CCCCCCNC(=O)NCCCN(CC(N)=O)C(=O)C(CCC(C)C)NC(=O)C(CN)NC(=O)C(Cc2ccc(O)cc2)NC1=O
|
1346 |
-
NC(CNc1ncc(-c2ccc3c(c2)CC(=O)N3)s1)Cc1ccc(C(F)(F)F)cc1
|
1347 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(F)c(F)c4F)c3)cc12
|
1348 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)cn1
|
1349 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)Cc1c[nH]c2ccccc12
|
1350 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)cc3)cn2CCN2CC3(COC3)C2)CC1
|
1351 |
-
COc1cc2ncc3c(N)nc(-c4cncc(OCC(N)CC(C)C)c4)cc3c2cc1OC
|
1352 |
-
Nc1ncccc1-c1nc2cc(-c3cccnc3)cnc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
1353 |
-
CC(C)CNCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
1354 |
-
CC1CN(c2ncc3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3n2)CCN1
|
1355 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)cc1Br
|
1356 |
-
NC1(c2ccc(-c3nc4ccc(-c5cnc[nH]5)cn4c3-c3ccccc3)cc2)CCC1
|
1357 |
-
Nc1ncccc1-c1nc2ccc(-c3cccnc3)nc2n1-c1ccc(CNC(=O)c2cccc(F)c2)cc1
|
1358 |
-
Cn1ncc(Cl)c1-c1oc(C(=O)NC2CNCCC2c2ccc(Cl)cc2)cc1Br
|
1359 |
-
NC1(C(=O)NC(CCCN2CCCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
1360 |
-
Cn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2-c2cncnc2)CC1
|
1361 |
-
NC1(c2ccc(-c3nc4c(-c5ccn[nH]5)cccn4c3-c3ccccc3)cc2)CCC1
|
1362 |
-
COc1cncc(-c2cccn3c(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc23)c1
|
1363 |
-
N#Cc1cnc(-c2ccc(CN3CCC(n4c(=O)[nH]c5ccccc54)CC3)cc2)c(-c2ccccc2)c1
|
1364 |
-
Cc1ccccc1-c1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2n1
|
1365 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCCCNCC(O)CO)c21
|
1366 |
-
Nc1ncnc2nc(-c3ccc(CN4CCC(n5ncc6c(N)ncnc65)CC4)cc3)c(-c3ccccc3)cc12
|
1367 |
-
NC1(c2ccc(-c3nc4c(-c5ccc6cn[nH]c6c5)cccn4c3-c3ccccc3)cc2)CCC1
|
1368 |
-
NC(=O)c1cccc(-c2ccc3c(c2)nn2cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nc32)c1
|
1369 |
-
CN(C)C(=O)COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1370 |
-
COCCOc1cc(F)ccc1NC(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
1371 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)cc1
|
1372 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)c(Cl)c1
|
1373 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cc(F)ccc3-4)cc2)C1
|
1374 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3cnc(F)c(Cl)c3)cn2CCN2CCC2)CC1
|
1375 |
-
Cc1c[nH]c2ncnc(N3CCC(NC(=O)Nc4ccccc4)C3)c12
|
1376 |
-
Nc1ccc(S(=O)(=O)Nc2nncs2)cc1
|
1377 |
-
c1ccc(-c2cn3nccc3nc2-c2ccc(CN3CCC(c4nc5cccnc5[nH]4)CC3)cc2)cc1
|
1378 |
-
Cn1ncc2ccc(-c3cnc(NCC(N)Cc4ccc(C(F)(F)F)cc4)s3)cc21
|
1379 |
-
NC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3c2CS(=O)(=O)C3)CC1
|
1380 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccc(CO)cc3-4)cc2)C1
|
1381 |
-
Cn1c(CC(=O)N2CCc3c(O)cccc32)nc(N2CCOCC2)cc1=O
|
1382 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNCCC2c2ccc(F)c(F)c2)oc1Cl.O=C(O)C(O)C(O)C(=O)O
|
1383 |
-
Cl.Cn1ncc(Cl)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)s1
|
1384 |
-
O=C1CC2OCC3=C(C(=O)c4ccccc4C3=O)C2O1
|
1385 |
-
COc1ccccc1NC(=O)NC1CCN(Cc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)CC1
|
1386 |
-
NC1(c2ccc(-c3nc4ccc(-c5ncc[nH]5)cn4c3-c3ccccc3)cc2)CCC1
|
1387 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(F)c4)cn3CCN3CCCC3)CC2)c1C1CCC1
|
1388 |
-
COc1ccc(C2(C(=O)N3CCN(c4ncnc5[nH]cc(Br)c45)CC3)CCNCC2)cc1
|
1389 |
-
CS(=O)(=O)c1ccc(-c2cnc3cc(-c4ccccc4)c(-c4ccc(C5(N)CCC5)cc4)nn23)cc1
|
1390 |
-
Cc1nc(N)nc2c1cc(-c1cnc3ccccc3c1)c(=O)n2C1CCC(OCO)CC1
|
1391 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2-c2ccc(C(=O)O)cc2)CC1
|
1392 |
-
COc1ccc(C2(C(=O)N3CCN(c4ncnc5[nH]ccc45)CC3)CCNCC2)cc1
|
1393 |
-
O=c1c(-c2ccc(O)cc2)coc2cc(O)cc(O)c12
|
1394 |
-
CCOCCN(CC(O)CN1CCCC2(CCc3cc4c(cc3O2)CNC4=O)C1)S(=O)(=O)c1c(C)cccc1C
|
1395 |
-
COc1cc(-c2cn[nH]c2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1396 |
-
Nc1ncnc2nc(-c3ccc(CN4CCC(n5ncc6c(N)ncnc65)CC4)cc3)c(-c3ccccc3)cc12
|
1397 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(C)N)cc21
|
1398 |
-
COc1cc2ncc3c(N)nc(-c4cncc(OCC(N)Cc5ccc(Cl)c(Cl)c5)c4)cc3c2cc1OC
|
1399 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)N1CCc2ncccc21
|
1400 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3ccco3)cc12
|
1401 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4c(F)cccc4F)c3)cc12
|
1402 |
-
c1ccc(-c2cc3cnc(-n4ccnc4)nc3nc2-c2ccc(CN3CCC(c4nnc(-c5ccccn5)[nH]4)CC3)cc2)cc1
|
1403 |
-
NC(CNc1ncc(-c2ccc3[nH]c(=O)oc3c2)s1)Cc1ccc(C(F)(F)F)cc1
|
1404 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3cccc(Cl)c3)cn2CCN2CCCC2)CC1
|
1405 |
-
CC(F)(C(=O)N1CCc2c(F)cccc21)c1nc(N2CCOCC2)cc(=O)[nH]1
|
1406 |
-
C=Cc1cccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1407 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(CN3CCCC3)cc21
|
1408 |
-
N#Cc1cccc2c1nn1cc(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc21
|
1409 |
-
Nc1nn(CCc2c[nH]cn2)c2nc(-c3ccc(CN4CCC(n5c(=O)[nH]c6ccccc65)CC4)cc3)c(-c3ccccc3)cc12
|
1410 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1cccc2ccccc12
|
1411 |
-
NC(COc1cncc(-c2ccc3c(c2)C(F)(F)C(=O)N3)c1)Cc1c[nH]c2ccccc12
|
1412 |
-
NC(COc1cncc(-c2ccc3c(F)nccc3c2)c1)Cc1c[nH]c2ccccc12
|
1413 |
-
C#Cc1cccc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c1
|
1414 |
-
NC1(C(=O)NCc2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
1415 |
-
N#Cc1ccc2nc(C3CCN(Cc4ccc(-c5nc6nccn6cc5-c5ccccc5)cc4)CC3)[nH]c2c1
|
1416 |
-
CC(NC(=O)CCCCCNC(=O)C1CC(O)C(n2cnc3c(N)ncnc32)C1)C(=O)NCCCCCC(=O)NC(CCCNC(=N)N)C(=O)NC(CCCNC(=N)N)C(N)=O
|
1417 |
-
NC1CCN(c2ncnc3[nH]cnc23)CC1
|
1418 |
-
NC(=O)Nc1ccc2c(c1)C(=Cc1ccc[nH]1)C(=O)N2
|
1419 |
-
NC(CNc1nnc(-c2ccc3[nH]nc(-c4ccccc4)c3c2)s1)Cc1cccc(C(F)(F)F)c1
|
1420 |
-
CC(=O)Nc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1421 |
-
CC(=C1C(=O)Nc2ccc(NC(N)=O)cc21)c1cc(CNC(=O)c2ccncc2)c[nH]1
|
1422 |
-
CC(=C1C(=O)Nc2ccc(NC(N)=O)cc21)c1ccc[nH]1
|
1423 |
-
CC(=O)Nc1nc2ccc(-c3cnc(Cl)c(NS(=O)(=O)c4cccc(C(F)(F)F)c4)c3)cc2s1
|
1424 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ncccc3-4)cc2)CC(O)(C2CC2)C1
|
1425 |
-
Cc1ccc(F)cc1CC(N)COc1cncc(-c2ccc3[nH]nc(C)c3c2)c1
|
1426 |
-
NC(c1ccc(Cl)cc1)C1CCN(c2ccnc3[nH]ccc23)CC1
|
1427 |
-
CCONC(=O)c1ccc2c(c1)-c1nc(-c3ccc(C4(N)CC(C)(O)C4)cc3)c(-c3ccccc3)n1CO2
|
1428 |
-
COC(=O)c1cnn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc12
|
1429 |
-
NC1(c2ccc(-c3nc4ccc(Br)cn4c3-c3ccccc3)cc2)CCC1
|
1430 |
-
COCCN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
1431 |
-
c1ccc(-c2cc3ccncc3nc2-c2ccc(CN3CCC(c4n[nH]c(-c5ccccn5)n4)CC3)cc2)cc1
|
1432 |
-
Cc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2n1
|
1433 |
-
NC(COc1cncc(C=Cc2ccncc2)c1)Cc1cccc(O)c1
|
1434 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3cccc(F)c3-4)cc2)CCC1
|
1435 |
-
CNc1nccc(-c2ccc(C(=O)NC(CCN)Cc3ccc(Cl)cc3Cl)s2)n1
|
1436 |
-
Cn1c(CC(=O)Nc2cccc(Br)c2)nc(N2CCOCC2)cc1=O
|
1437 |
-
Cc1ccc(-n2c(-c3cccnc3N)nc3cccnc32)cc1
|
1438 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c(C(N)=O)[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2n1
|
1439 |
-
CCCC1NC(=O)C(CCCNC(=N)N)NC(=O)CN(C(=O)C(N)CCCNC(=N)N)CCNC(=O)NCCCCCCN(CC(N)=O)C(=O)C(CCC(C)C)NC(=O)C(CN)NC(=O)C(Cc2ccc(O)cc2)NC1=O
|
1440 |
-
c1ccc(-n2cc(COCc3ccc(-c4nnc5n4-c4cccnc4Nc4ccccc4-5)cc3)nn2)cc1
|
1441 |
-
NC1(c2ccc(-c3nc4c5ccc(Br)cc5nn4cc3-c3ccccc3)cc2)CCC1
|
1442 |
-
c1ccc2c(CC(COc3cncc(-c4ccc5nonc5c4)c3)Nc3ccc4nonc4c3)c[nH]c2c1
|
1443 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(F)c3)cn2CCN2CCCC2)CC1
|
1444 |
-
NC(COc1cncc(-c2ccc3[nH]c(=O)sc3c2)c1)Cc1c[nH]c2ccccc12
|
1445 |
-
CCn1c(-c2nonc2N)nc2cncc(OC3CCNCC3)c21
|
1446 |
-
NC1(c2ccc(-c3nc4c(F)cc(F)cn4c3-c3ccccc3)cc2)CCC1
|
1447 |
-
NC(COc1cncc(-c2ccc3cnccc3c2)c1)CC1CCCCC1
|
1448 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ncccc3-4)cc2)C1
|
1449 |
-
CC(C)NCC(C(=O)N1CCN(c2ncnc3c2C(C)CC3O)CC1)c1ccc(Cl)cc1
|
1450 |
-
CNC(=O)Nc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1451 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OC(CN)c3ccccc3)cc21
|
1452 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccccc1
|
1453 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1cccc(OC(F)(F)C(F)F)c1
|
1454 |
-
COc1ccc(S(=O)(=O)Nc2cc(-c3ccc4nc(NC(C)=O)sc4c3)cnc2Cl)cc1
|
1455 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccccc1
|
1456 |
-
NC(CNc1cncc(C=Cc2ccncc2)c1)Cc1c[nH]c2ccccc12
|
1457 |
-
CC1Cc2ccccc2N1C(=S)Cc1nc(N2CCOCC2)cc(=S)[nH]1
|
1458 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)cc3)cn2CCN2CCCC2)CC1
|
1459 |
-
NC1(c2ccc(-c3nc4cc(-c5nn[nH]n5)ccn4c3-c3ccccc3)cc2)CCC1
|
1460 |
-
CN(C)CCNC(=O)c1c[nH]c(C=C2C(=O)Nc3ccc(NC(N)=O)cc32)c1
|
1461 |
-
COc1cc(-c2ncc[nH]2)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1462 |
-
CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CC4CN(C)CC4C3)cn2)cs1)C(F)(F)F
|
1463 |
-
Nc1ncccc1-c1nc2cccnc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
1464 |
-
O=C(NC(c1ccc(Cl)c(Cl)c1)C1CCNCC1)c1ccc2cnc(F)cc2c1
|
1465 |
-
Nc1ccc2ncnc(N3CCN(C(=O)C(N)Cc4ccc(Cl)cc4)CC3)c2c1
|
1466 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)C(CC1CCCCC1)NC(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(N)=O
|
1467 |
-
NC1(c2ccc(-c3nc4cc(-n5cccn5)ccn4c3-c3ccccc3)cc2)CCC1
|
1468 |
-
CC(C)CCCC1(C)CCc2cc(S(N)(=O)=O)cc(Br)c2O1
|
1469 |
-
NC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3[nH]cc(C4CC4)c23)CC1
|
1470 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CC(=O)O)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
1471 |
-
NC(CNc1ncc(-c2ccc3cnccc3c2)s1)Cc1ccc(C(F)(F)F)cc1
|
1472 |
-
Cn1cncc1-c1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1
|
1473 |
-
COCCNCCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C(N)=O)CC1
|
1474 |
-
CC(=O)Nc1ccc(-c2cncc(OCC(N)Cc3c[nH]c4ccccc34)c2)cc1
|
1475 |
-
NC(Cc1cccc2ccccc12)C(=O)Nc1cncc(C=Cc2ccncc2)c1
|
1476 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1cc2c(C)n[nH]c2cn1
|
1477 |
-
CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CCC4(CC3)COC4)cn2)cs1)C(F)(F)F
|
1478 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccc(F)c(C(F)(F)F)c4)cn3CC3CCN3)CC2)c1Cl
|
1479 |
-
C=Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(F)c3)cn2CCN2CCCC2)CC1
|
1480 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC2CNC(CC(N)=O)CC2c2ccc(Cl)c(Cl)c2)oc1Cl
|
1481 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ccncc3-4)cc2)CCC1
|
1482 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccnc(Cl)c3)cn2CCN2CCC2)CC1
|
1483 |
-
COC(=O)c1cnn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CCC(c5n[nH]c(-c6cccc(C)n6)n5)CC4)cc3)nc12
|
1484 |
-
CCN(c1nccc(-c2ccc3nc(NC(C)=O)sc3c2)n1)S(=O)(=O)c1ccc(OC)cc1
|
1485 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(N)=O)c3)cn2CCN2CCC2)CC1
|
1486 |
-
NC(COc1cncc(C=Cc2ccnc(NCc3ccccc3)c2)c1)Cc1c[nH]c2ccccc12
|
1487 |
-
Cc1occc1-c1nc(N)c(OCC(N)Cc2c[nH]c3ccccc23)cc1-c1cnc2[nH]nc(C)c2c1
|
1488 |
-
CC(C)NCC(Cc1ccc(Cl)cc1)C(=O)N1CCN(c2ncnc3c2C(C)OC3)CC1
|
1489 |
-
NC1(c2ccc(-c3nc4nc(-c5ccccc5)ccn4c3-c3ccccc3)cc2)CCC1
|
1490 |
-
Cc1cnn(C)c1-c1ccc(C(=O)NC2CNCCC2c2cccc(F)c2)s1.Cl
|
1491 |
-
O=c1[nH]c2ccccc2n1C1CCN(Cc2ccc(-c3nc4ccc(-c5nn[nH]n5)cc4nc3-c3ccccc3)cc2)CC1
|
1492 |
-
O=C(Cc1nc(N2CCOCC2)cc(=O)[nH]1)Nc1ccc(F)c(F)c1F
|
1493 |
-
c1ccc(-c2cc3cccnc3nc2-c2ccc(CN3CCC(c4cc(-c5ccncc5)[nH]n4)CC3)cc2)cc1
|
1494 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2ccc(F)c(F)c2)oc1Cl
|
1495 |
-
CC(C)Nc1c(-c2ccccc2)c(-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)nc2nc(-c3ccccn3)nn12
|
1496 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccccc4F)C3)c12
|
1497 |
-
CCN(CC)CC1CN(C(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c2ccccc21
|
1498 |
-
NC1(c2ccc(-c3nn4c(Br)cnc4cc3-c3ccccc3)cc2)CCC1
|
1499 |
-
CCNC(=O)NC1CCN(Cc2ccc(-c3nc4nc(SC)ncc4cc3-c3ccccc3)cc2)CC1
|
1500 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCCCC2)CC1
|
1501 |
-
Cc1noc(C)c1S(=O)(=O)N(CCOC(C)C)CC(O)CN1CCCC2(CC(=O)c3cc(O)ccc3O2)C1
|
1502 |
-
Cc1cccc(-c2nc(C3CCN(Cc4ccc(-c5nc6nc(C)nn6cc5-c5cccc(F)c5)cc4)CC3)n[nH]2)n1
|
1503 |
-
CCc1cc2cc(-c3cncc(OCC(N)Cc4c[nH]c5ccccc45)c3)ccc2cn1
|
1504 |
-
CN(C)CC1CN(C(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c2ccccc21
|
1505 |
-
CCOCCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN(C)C)CC1
|
1506 |
-
Cc1ccc(CC(N)COc2cncc(-c3ccc4[nH]nc(C)c4c3)c2)cc1F
|
1507 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccsc3)COc3cccc(F)c3-4)cc2)CC(O)(C2CC2)C1
|
1508 |
-
Clc1c[nH]c2ncnc(N3CC4(CCNCC4)c4ccccc43)c12
|
1509 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)c3ccccc3)cc21
|
1510 |
-
NC(CNc1ncc(-c2ccc3c(c2)CNC3=O)s1)Cc1ccc(C(F)(F)F)cc1
|
1511 |
-
COc1cc2ncc3c(N)nc(-c4cncc(OCC(N)Cc5ccccc5)c4)cc3c2cc1OC
|
1512 |
-
COc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c1C#N
|
1513 |
-
COc1nc(Br)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1514 |
-
Cn1c(CC(=O)N2CCc3c2ccc(F)c3F)nc(N2CCOCC2)cc1=O
|
1515 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CC2CCN2)CC1
|
1516 |
-
c1ccc(-c2cc3cnc(N4CCn5cnnc5C4)nc3nc2-c2ccc(CN3CCC(c4nnc(-c5ccccn5)[nH]4)CC3)cc2)cc1
|
1517 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCCCC2)CC1
|
1518 |
-
Nc1ncnc(N2CCN(C(=O)C(N)Cc3ccc(Cl)cc3)CC2)c1Br
|
1519 |
-
Cl.Cn1ncc(Cl)c1-c1csc(C(=O)NC2CNCCC2c2ccc(Cl)c(C(F)(F)F)c2)c1
|
1520 |
-
NC(COc1cncc(C=Cc2ccncc2)c1)Cc1c[nH]c2ccccc12
|
1521 |
-
Cn1c(CC(=O)N2CCc3c(Cl)cccc32)nc(N2CCOCC2)cc1=S
|
1522 |
-
O=C(N1CCN(c2ncnc3[nH]nc(Br)c23)CC1)C1(c2ccc(Cl)c(Cl)c2)CCNCC1
|
1523 |
-
COc1cccc(C(=O)Nc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)c1
|
1524 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
1525 |
-
N#Cc1c(N)ncnc1N1CCC(c2nc(-c3cnoc3)cn2CCN2CCC2)CC1
|
1526 |
-
CC(C)(C)C(=O)N1CCN(c2cccc(-c3ccc4nc(-c5cccnc5N)n(-c5ccc(C6(N)CCC6)cc5)c4n3)c2)CC1
|
1527 |
-
CNC(=O)Nc1ccc(CNc2ncsc2C(=O)Nc2ccc3c(c2)OC(F)(F)O3)cn1
|
1528 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)c4ccccc4)c3)cc12
|
1529 |
-
Cl.NCc1ccc(-n2c(-c3cccnc3N)nc3ccc(-c4cn[nH]c4)nc32)cc1
|
1530 |
-
Cn1ncc(Cl)c1-c1cc(C(=O)NC(CN)Cc2cccc(F)c2)sc1Cl
|
1531 |
-
Cc1cc(C(N)=O)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1532 |
-
Nc1ncccc1-c1nc2ccc(Nc3ccc(N4CCOCC4)cc3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
1533 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)nc(OCC(N)c3ccccc3)cc21
|
1534 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1cc2ccccc2s1
|
1535 |
-
COCCC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
1536 |
-
Nc1cc2cc(-c3cnc(NCC(N)Cc4ccc(C(F)(F)F)cc4)s3)ccc2cn1
|
1537 |
-
NC1(c2ccc(-c3nn4c(-c5ccn[nH]5)cnc4cc3-c3ccccc3)cc2)CCC1
|
1538 |
-
NC1(C(=O)NC(CCCN2CCOCC2)c2ccc(Cl)cc2)CCN(c2ncnc3[nH]ccc23)CC1
|
1539 |
-
CCc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN(C)C)CC1
|
1540 |
-
ClCc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6n[nH]c(-c7ccccn7)n6)CC5)cc4)nc3ccn12
|
1541 |
-
Cc1cccc(-c2nc(C3CN(Cc4ccc(-c5nc6nc(C)c(Br)n6cc5-c5ccccc5)cc4)C3)n[nH]2)n1
|
1542 |
-
CCOc1cccc2c1-c1nc(-c3ccc(C4(N)CC(C)(O)C4)cc3)c(-c3ccccc3)n1CO2
|
1543 |
-
CCOc1cccc2c1-c1nc(-c3ccc(C4(N)CC(C)(O)C4)cc3)c(-c3ccccc3)n1CO2
|
1544 |
-
Cc1ccc2c(c1)-c1nnc(-c3ccc(C4(N)CCC4)cc3)n1-c1cccnc1N2
|
1545 |
-
COC(=O)c1cn2cc(-c3ccccc3)c(-c3ccc(CN4CC(c5n[nH]c(-c6ccccn6)n5)C4)cc3)nc2n1
|
1546 |
-
Nc1noc2ccc(-c3cnc(NCC(N)Cc4ccc(C(F)(F)F)cc4)s3)cc12
|
1547 |
-
CCn1c(CC(=O)Nc2ccc(F)cc2)nc(N2CCOCC2)cc1=O
|
1548 |
-
COC(=O)c1cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2c(OC)n1
|
1549 |
-
Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4cccc(F)c4F)c3)cc12
|
1550 |
-
CC(NC(=O)C1(N)CCN(c2ncnc3[nH]ccc23)CC1)c1ccc(Cl)cc1
|
1551 |
-
Cc1n[nH]c2cnc(-c3cc(OCC(N)Cc4c[nH]c5ccccc45)cnc3-c3ccoc3)nc12
|
1552 |
-
COc1cc(F)ccc1NC(=O)Cc1nc(N2CCOCC2)cc(=O)[nH]1
|
1553 |
-
Nc1ncnc2nc(-c3ccc(CN4CCC(c5nc6ccc(F)cc6[nH]5)CC4)cc3)c(-c3ccccc3)cc12
|
1554 |
-
CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CC(C)(C#N)C3)cn2)cs1)C(F)(F)F
|
1555 |
-
CN(C)CCNC(=O)c1cccc(-c2c[nH]c(C=C3C(=O)Nc4ccc(NC(N)=O)cc43)c2)c1
|
1556 |
-
NC1(c2ccc(-c3nc4n(c3-c3ccsc3)COc3ccccc3-4)cc2)CCC1
|
1557 |
-
Cc1c[nH]c2ncnc(N3CCC(N)(CNC(=O)c4ccc(F)cc4F)C3)c12
|
1558 |
-
Cc1cc(Cl)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1559 |
-
CNc1c(-c2ccccc2)c(-c2ccc(CN3CC(c4n[nH]c(-c5ccccn5)n4)C3)cc2)nc2ncc(Br)n12
|
1560 |
-
Nc1ncccc1-c1nc2ccc(-c3ccccc3)nc2n1-c1ccc(CNC(=O)c2ccccc2)cc1
|
1561 |
-
Cn1c(CC(=O)N2CCc3c(OC(F)F)cccc32)nc(N2CCOCC2)cc1=O
|
1562 |
-
NC1(c2ccc(-c3nc4c5cc(-c6ccc(F)c(CO)c6)ccc5nn4cc3-c3ccccc3)cc2)CCC1
|
1563 |
-
Cc1c(NCCN(C)C)cc(Cl)cc1N1CCN(c2ncnc3[nH]nc(Br)c23)CC1
|
1564 |
-
CC1SCc2ncnc(N3CCN(C(=O)C(Cc4ccc(Cl)cc4)C4(N)CC4)CC3)c21
|
1565 |
-
NC(=O)c1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CC2CNC2)CC1
|
1566 |
-
Nc1ncccc1-c1nc2ccc(-c3cccc(N4CCC(C(=O)N5CCOCC5)CC4)c3)nc2n1-c1ccc(C2(N)CCC2)cc1
|
1567 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1ccc(F)c(F)c1
|
1568 |
-
NC1(c2ccc(-c3nc4c5cc(-c6cn[nH]c6)ccc5nn4cc3-c3ccccc3)cc2)CCC1
|
1569 |
-
CCOc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1570 |
-
CC(=O)Nc1ccc2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1571 |
-
Cc1c[nH]c2ncnc(N3CCN(C(=O)C(N)Cc4ccc(F)cc4)CC3)c12
|
1572 |
-
Cc1cccc(NC(=O)Cc2nc(N3CCOCC3)cc(=O)[nH]2)c1O
|
1573 |
-
NC1(c2ccc(-c3nc4cc(-c5ccncc5)ccn4c3-c3ccccc3)cc2)CCC1
|
1574 |
-
COC(=O)c1cnn2cc(-c3c(F)cccc3F)c(-c3ccc(CN4CCC(c5n[nH]c(-c6cccc(C)n6)n5)CC4)cc3)nc12
|
1575 |
-
NC(c1ccc(Cl)cc1)C1CCN(c2ncnc3[nH]cnc23)CC1
|
1576 |
-
Nc1ncnc(N2CCC(c3nc(-c4ccnc(C(F)(F)F)c4)cn3CCN3CCC3)CC2)c1-c1cnoc1
|
1577 |
-
Cc1nc(N)nc2c1cc(-c1cn[nH]c1)c(=O)n2C1CCC(OCC(N)=O)CC1
|
1578 |
-
COc1ccc(S(=O)(=O)NCc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)cc1
|
1579 |
-
CC(C)NC(c1ccc(Cl)cc1)c1ccc(-c2ncnc3[nH]cnc23)cc1
|
1580 |
-
Cc1cccc2c1CCN2C(=O)Cc1nc(N2CCOCC2)cc(=O)n1C
|
1581 |
-
Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3ccc[nH]3)cc12
|
1582 |
-
O=C1Nc2ccccc2C1=Cc1c[nH]nc1-c1ccccc1[N+](=O)[O-]
|
1583 |
-
O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1cnc2ccccc2n1
|
1584 |
-
CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CC(N4CCOCC4)C3)cn2)cs1)C(F)(F)F
|
1585 |
-
NC(=O)c1cc(Cl)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
|
1586 |
-
CCOc1cccc2c1-c1nc(-c3ccc(C4(N)CC(O)(C5CC5)C4)cc3)c(-c3ccccc3)n1CO2
|
1587 |
-
CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ncccc3-4)cc2)C1
|
1588 |
-
Clc1cnc2nc(-c3ccc(CN4CC(c5nnc(-c6ccccn6)[nH]5)C4)cc3)c(-c3ccccc3)cn12
|
1589 |
-
O=C(NC(c1ccc(Cl)c(Cl)c1)C1CCNC1)c1ccc2cnccc2c1
|
1590 |
-
CC(C)CCCC1(C)CCc2cc(O)cc(F)c2O1
|
1591 |
-
COCCOc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2n1
|
1592 |
-
CCCC(NC(=O)C(CCCNC(=N)N)NC(=O)CN(CCN)C(=O)C(N)CCCNC(=N)N)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CN)C(=O)NC(CCC(C)C)C(=O)N(CCCCN)CC(N)=O
|
1593 |
-
CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCCCN)c21
|
1594 |
-
COc1cc(Br)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
|
1595 |
-
NCCC(NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1)c1ccccc1
|
1596 |
-
NC(Cc1ccc(Br)cc1)C(=O)N1CCN(c2ncnc3ccccc23)CC1
|
1597 |
-
O=C(NC(c1ccc(Cl)c(Cl)c1)C1CCNCC1)c1ccc2cnccc2c1
|
1598 |
-
[C-]#[N+]c1ccc(C(=O)Nc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)cc1
|
1599 |
-
CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C2CC2)CC1
|
1600 |
-
Cc1ccc(CC(N)C(=O)N2CCN(c3ncnc4ccccc34)CC2)cc1
|
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|
experiments/.gitignore
DELETED
@@ -1 +0,0 @@
|
|
1 |
-
inference/
|
|
|
|
gradio_app.py
DELETED
@@ -1,208 +0,0 @@
|
|
1 |
-
import gradio as gr
|
2 |
-
from trainer import Trainer
|
3 |
-
import PIL
|
4 |
-
from PIL import Image
|
5 |
-
import pandas as pd
|
6 |
-
import random
|
7 |
-
from rdkit import Chem
|
8 |
-
from rdkit.Chem import Draw
|
9 |
-
from rdkit.Chem.Draw import IPythonConsole
|
10 |
-
import shutil
|
11 |
-
|
12 |
-
class DrugGENConfig:
|
13 |
-
submodel='CrossLoss'
|
14 |
-
act='relu'
|
15 |
-
z_dim=16
|
16 |
-
max_atom=45
|
17 |
-
lambda_gp=1
|
18 |
-
dim=128
|
19 |
-
depth=1
|
20 |
-
heads=8
|
21 |
-
dec_depth=1
|
22 |
-
dec_heads=8
|
23 |
-
dec_dim=128
|
24 |
-
mlp_ratio=3
|
25 |
-
warm_up_steps=0
|
26 |
-
dis_select='mlp'
|
27 |
-
init_type='normal'
|
28 |
-
batch_size=128
|
29 |
-
epoch=50
|
30 |
-
g_lr=0.00001
|
31 |
-
d_lr=0.00001
|
32 |
-
g2_lr=0.00001
|
33 |
-
d2_lr=0.00001
|
34 |
-
dropout=0.
|
35 |
-
dec_dropout=0.
|
36 |
-
n_critic=1
|
37 |
-
beta1=0.9
|
38 |
-
beta2=0.999
|
39 |
-
resume_iters=None
|
40 |
-
clipping_value=2
|
41 |
-
features=False
|
42 |
-
test_iters=10_000
|
43 |
-
num_test_epoch=30_000
|
44 |
-
inference_sample_num=1000
|
45 |
-
num_workers=1
|
46 |
-
mode="inference"
|
47 |
-
inference_iterations=100
|
48 |
-
inf_batch_size=1
|
49 |
-
protein_data_dir='data/akt'
|
50 |
-
drug_index='data/drug_smiles.index'
|
51 |
-
drug_data_dir='data/akt'
|
52 |
-
mol_data_dir='data'
|
53 |
-
log_dir='experiments/logs'
|
54 |
-
model_save_dir='experiments/models'
|
55 |
-
# inference_model=""
|
56 |
-
sample_dir='experiments/samples'
|
57 |
-
result_dir="experiments/tboard_output"
|
58 |
-
dataset_file="chembl45_train.pt"
|
59 |
-
drug_dataset_file="akt_train.pt"
|
60 |
-
raw_file='data/chembl_train.smi'
|
61 |
-
drug_raw_file="data/akt_train.smi"
|
62 |
-
inf_dataset_file="chembl45_test.pt"
|
63 |
-
inf_drug_dataset_file='akt_test.pt'
|
64 |
-
inf_raw_file='data/chembl_test.smi'
|
65 |
-
inf_drug_raw_file="data/akt_test.smi"
|
66 |
-
log_sample_step=1000
|
67 |
-
set_seed=True
|
68 |
-
seed=1
|
69 |
-
resume=False
|
70 |
-
resume_epoch=None
|
71 |
-
resume_iter=None
|
72 |
-
resume_directory=None
|
73 |
-
|
74 |
-
class ProtConfig(DrugGENConfig):
|
75 |
-
submodel="Prot"
|
76 |
-
inference_model="experiments/models/Prot"
|
77 |
-
|
78 |
-
class CrossLossConfig(DrugGENConfig):
|
79 |
-
submodel="CrossLoss"
|
80 |
-
inference_model="experiments/models/CrossLoss"
|
81 |
-
|
82 |
-
class NoTargetConfig(DrugGENConfig):
|
83 |
-
submodel="NoTarget"
|
84 |
-
inference_model="experiments/models/NoTarget"
|
85 |
-
|
86 |
-
|
87 |
-
model_configs = {
|
88 |
-
"Prot": ProtConfig(),
|
89 |
-
"DrugGEN": CrossLossConfig(),
|
90 |
-
"DrugGEN-NoTarget": NoTargetConfig(),
|
91 |
-
}
|
92 |
-
|
93 |
-
|
94 |
-
|
95 |
-
def function(model_name: str, mol_num: int, seed: int) -> tuple[PIL.Image, pd.DataFrame, str]:
|
96 |
-
'''
|
97 |
-
Returns:
|
98 |
-
image, score_df, file path
|
99 |
-
'''
|
100 |
-
|
101 |
-
config = model_configs[model_name]
|
102 |
-
config.inference_sample_num = mol_num
|
103 |
-
config.seed = seed
|
104 |
-
|
105 |
-
trainer = Trainer(config)
|
106 |
-
scores = trainer.inference() # create scores_df out of this
|
107 |
-
|
108 |
-
score_df = pd.DataFrame(scores, index=[0])
|
109 |
-
old_model_names = {
|
110 |
-
"DrugGEN": "CrossLoss",
|
111 |
-
"DrugGEN-NoTarget": "NoTarget",
|
112 |
-
}
|
113 |
-
output_file_path = f'experiments/inference/{old_model_names[model_name]}/inference_drugs.txt'
|
114 |
-
|
115 |
-
import os
|
116 |
-
new_path = f'{model_name}_denovo_mols.smi'
|
117 |
-
os.rename(output_file_path, new_path)
|
118 |
-
|
119 |
-
with open(new_path) as f:
|
120 |
-
inference_drugs = f.read()
|
121 |
-
|
122 |
-
generated_molecule_list = inference_drugs.split("\n")
|
123 |
-
|
124 |
-
rng = random.Random(seed)
|
125 |
-
|
126 |
-
selected_molecules = rng.choices(generated_molecule_list,k=12)
|
127 |
-
selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules]
|
128 |
-
|
129 |
-
drawOptions = Draw.rdMolDraw2D.MolDrawOptions()
|
130 |
-
drawOptions.prepareMolsBeforeDrawing = False
|
131 |
-
drawOptions.bondLineWidth = 0.5
|
132 |
-
|
133 |
-
molecule_image = Draw.MolsToGridImage(
|
134 |
-
selected_molecules,
|
135 |
-
molsPerRow=3,
|
136 |
-
subImgSize=(400, 400),
|
137 |
-
maxMols=len(selected_molecules),
|
138 |
-
# legends=None,
|
139 |
-
returnPNG=False,
|
140 |
-
drawOptions=drawOptions,
|
141 |
-
highlightAtomLists=None,
|
142 |
-
highlightBondLists=None,
|
143 |
-
)
|
144 |
-
|
145 |
-
|
146 |
-
return molecule_image, score_df, new_path
|
147 |
-
|
148 |
-
|
149 |
-
|
150 |
-
with gr.Blocks() as demo:
|
151 |
-
with gr.Row():
|
152 |
-
with gr.Column(scale=1):
|
153 |
-
gr.Markdown("# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
|
154 |
-
with gr.Row():
|
155 |
-
gr.Markdown("[](https://arxiv.org/abs/2302.07868)")
|
156 |
-
gr.Markdown("[](https://github.com/HUBioDataLab/DrugGEN)")
|
157 |
-
|
158 |
-
with gr.Accordion("Expand to display information about models", open=False):
|
159 |
-
gr.Markdown("""
|
160 |
-
### Model Variations
|
161 |
-
- **DrugGEN**: composed of one GAN, the input of the GAN1 generator is the real molecules dataset and the GAN1 discriminator compares the generated molecules with the real inhibitors of the given target.
|
162 |
-
- **DrugGEN-NoTarget**: composed of one GAN, focuses on learning the chemical properties from the ChEMBL training dataset, no target-specific generation.
|
163 |
-
""")
|
164 |
-
model_name = gr.Radio(
|
165 |
-
choices=("DrugGEN", "DrugGEN-NoTarget"),
|
166 |
-
value="DrugGEN",
|
167 |
-
label="Select a model to make inference",
|
168 |
-
info=" DrugGEN-Prot and DrugGEN-CrossLoss models design molecules to target the AKT1 protein"
|
169 |
-
)
|
170 |
-
|
171 |
-
num_molecules = gr.Number(
|
172 |
-
label="Number of molecules to generate",
|
173 |
-
precision=0, # integer input
|
174 |
-
minimum=1,
|
175 |
-
value=1000,
|
176 |
-
maximum=10_000,
|
177 |
-
)
|
178 |
-
seed_num = gr.Number(
|
179 |
-
label="RNG seed value (can be used for reproducibility):",
|
180 |
-
precision=0, # integer input
|
181 |
-
minimum=0,
|
182 |
-
value=42,
|
183 |
-
)
|
184 |
-
|
185 |
-
submit_button = gr.Button(
|
186 |
-
value="Start Generating"
|
187 |
-
)
|
188 |
-
|
189 |
-
with gr.Column(scale=2):
|
190 |
-
scores_df = gr.Dataframe(
|
191 |
-
label="Scores",
|
192 |
-
headers=["Runtime (seconds)", "Validity", "Uniqueness", "Novelty (Train)", "Novelty (Inference)"],
|
193 |
-
)
|
194 |
-
file_download = gr.File(
|
195 |
-
label="Click to download generated molecules",
|
196 |
-
)
|
197 |
-
image_output = gr.Image(
|
198 |
-
label="Structures of randomly selected 12 de novo molecules from the inference set:"
|
199 |
-
)
|
200 |
-
# ).style(
|
201 |
-
# height=200*4,
|
202 |
-
# width=200*3,
|
203 |
-
# )
|
204 |
-
|
205 |
-
submit_button.click(function, inputs=[model_name, num_molecules, seed_num], outputs=[image_output, scores_df, file_download], api_name="inference")
|
206 |
-
|
207 |
-
demo.queue(concurrency_count=1)
|
208 |
-
demo.launch()
|
|
|
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|
layers.py
DELETED
@@ -1,265 +0,0 @@
|
|
1 |
-
import torch
|
2 |
-
import torch.nn as nn
|
3 |
-
from torch.nn import functional as F
|
4 |
-
import math
|
5 |
-
|
6 |
-
class MLP(nn.Module):
|
7 |
-
def __init__(self, in_feat, hid_feat=None, out_feat=None,
|
8 |
-
dropout=0.):
|
9 |
-
super().__init__()
|
10 |
-
if not hid_feat:
|
11 |
-
hid_feat = in_feat
|
12 |
-
if not out_feat:
|
13 |
-
out_feat = in_feat
|
14 |
-
self.fc1 = nn.Linear(in_feat, hid_feat)
|
15 |
-
self.act = torch.nn.ReLU()
|
16 |
-
self.fc2 = nn.Linear(hid_feat,out_feat)
|
17 |
-
self.droprateout = nn.Dropout(dropout)
|
18 |
-
|
19 |
-
def forward(self, x):
|
20 |
-
x = self.fc1(x)
|
21 |
-
x = self.act(x)
|
22 |
-
x = self.fc2(x)
|
23 |
-
return self.droprateout(x)
|
24 |
-
|
25 |
-
class Attention_new(nn.Module):
|
26 |
-
def __init__(self, dim, heads, attention_dropout=0.):
|
27 |
-
super().__init__()
|
28 |
-
assert dim % heads == 0
|
29 |
-
self.heads = heads
|
30 |
-
self.scale = 1./dim**0.5
|
31 |
-
|
32 |
-
self.q = nn.Linear(dim, dim)
|
33 |
-
self.k = nn.Linear(dim, dim)
|
34 |
-
self.v = nn.Linear(dim, dim)
|
35 |
-
self.e = nn.Linear(dim, dim)
|
36 |
-
#self.attention_dropout = nn.Dropout(attention_dropout)
|
37 |
-
|
38 |
-
self.d_k = dim // heads
|
39 |
-
self.heads = heads
|
40 |
-
self.out_e = nn.Linear(dim,dim)
|
41 |
-
self.out_n = nn.Linear(dim, dim)
|
42 |
-
|
43 |
-
|
44 |
-
def forward(self, node, edge):
|
45 |
-
b, n, c = node.shape
|
46 |
-
|
47 |
-
|
48 |
-
q_embed = self.q(node).view(-1, n, self.heads, c//self.heads)
|
49 |
-
k_embed = self.k(node).view(-1, n, self.heads, c//self.heads)
|
50 |
-
v_embed = self.v(node).view(-1, n, self.heads, c//self.heads)
|
51 |
-
|
52 |
-
e_embed = self.e(edge).view(-1, n, n, self.heads, c//self.heads)
|
53 |
-
|
54 |
-
q_embed = q_embed.unsqueeze(2)
|
55 |
-
k_embed = k_embed.unsqueeze(1)
|
56 |
-
|
57 |
-
attn = q_embed * k_embed
|
58 |
-
|
59 |
-
attn = attn/ math.sqrt(self.d_k)
|
60 |
-
|
61 |
-
|
62 |
-
attn = attn * (e_embed + 1) * e_embed
|
63 |
-
|
64 |
-
edge = self.out_e(attn.flatten(3))
|
65 |
-
|
66 |
-
attn = F.softmax(attn, dim=2)
|
67 |
-
|
68 |
-
v_embed = v_embed.unsqueeze(1)
|
69 |
-
|
70 |
-
v_embed = attn * v_embed
|
71 |
-
|
72 |
-
v_embed = v_embed.sum(dim=2).flatten(2)
|
73 |
-
|
74 |
-
node = self.out_n(v_embed)
|
75 |
-
|
76 |
-
return node, edge
|
77 |
-
|
78 |
-
class Encoder_Block(nn.Module):
|
79 |
-
def __init__(self, dim, heads,act, mlp_ratio=4, drop_rate=0.):
|
80 |
-
super().__init__()
|
81 |
-
self.ln1 = nn.LayerNorm(dim)
|
82 |
-
|
83 |
-
self.attn = Attention_new(dim, heads, drop_rate)
|
84 |
-
self.ln3 = nn.LayerNorm(dim)
|
85 |
-
self.ln4 = nn.LayerNorm(dim)
|
86 |
-
self.mlp = MLP(dim, dim*mlp_ratio, dim, dropout=drop_rate)
|
87 |
-
self.mlp2 = MLP(dim, dim*mlp_ratio, dim, dropout=drop_rate)
|
88 |
-
self.ln5 = nn.LayerNorm(dim)
|
89 |
-
self.ln6 = nn.LayerNorm(dim)
|
90 |
-
|
91 |
-
def forward(self, x,y):
|
92 |
-
x1 = self.ln1(x)
|
93 |
-
x2,y1 = self.attn(x1,y)
|
94 |
-
x2 = x1 + x2
|
95 |
-
y2 = y1 + y
|
96 |
-
x2 = self.ln3(x2)
|
97 |
-
y2 = self.ln4(y2)
|
98 |
-
|
99 |
-
x = self.ln5(x2 + self.mlp(x2))
|
100 |
-
y = self.ln6(y2 + self.mlp2(y2))
|
101 |
-
return x, y
|
102 |
-
|
103 |
-
|
104 |
-
class TransformerEncoder(nn.Module):
|
105 |
-
def __init__(self, dim, depth, heads, act, mlp_ratio=4, drop_rate=0.1):
|
106 |
-
super().__init__()
|
107 |
-
|
108 |
-
self.Encoder_Blocks = nn.ModuleList([
|
109 |
-
Encoder_Block(dim, heads, act, mlp_ratio, drop_rate)
|
110 |
-
for i in range(depth)])
|
111 |
-
|
112 |
-
def forward(self, x,y):
|
113 |
-
|
114 |
-
for Encoder_Block in self.Encoder_Blocks:
|
115 |
-
x, y = Encoder_Block(x,y)
|
116 |
-
|
117 |
-
return x, y
|
118 |
-
|
119 |
-
class enc_dec_attention(nn.Module):
|
120 |
-
def __init__(self, dim, heads, attention_dropout=0., proj_dropout=0.):
|
121 |
-
super().__init__()
|
122 |
-
self.dim = dim
|
123 |
-
self.heads = heads
|
124 |
-
self.scale = 1./dim**0.5
|
125 |
-
|
126 |
-
|
127 |
-
"query is molecules"
|
128 |
-
"key is prot"
|
129 |
-
"values is again molecule"
|
130 |
-
self.q_mx = nn.Linear(dim,dim)
|
131 |
-
self.k_px = nn.Linear(dim,dim)
|
132 |
-
self.v_mx = nn.Linear(dim,dim)
|
133 |
-
|
134 |
-
|
135 |
-
self.k_pa = nn.Linear(dim,dim)
|
136 |
-
self.v_ma = nn.Linear(dim,dim)
|
137 |
-
|
138 |
-
|
139 |
-
|
140 |
-
|
141 |
-
|
142 |
-
#self.dropout_dec = nn.Dropout(proj_dropout)
|
143 |
-
self.out_nd = nn.Linear(dim, dim)
|
144 |
-
self.out_ed = nn.Linear(dim,dim)
|
145 |
-
|
146 |
-
def forward(self, mol_annot, prot_annot, mol_adj, prot_adj):
|
147 |
-
|
148 |
-
b, n, c = mol_annot.shape
|
149 |
-
_, m, _ = prot_annot.shape
|
150 |
-
|
151 |
-
|
152 |
-
query_mol_annot = self.q_mx(mol_annot).view(-1,m, self.heads, c//self.heads)
|
153 |
-
key_prot_annot = self.k_px(prot_annot).view(-1,n, self.heads, c//self.heads)
|
154 |
-
value_mol_annot = self.v_mx(mol_annot).view(-1,m, self.heads, c//self.heads)
|
155 |
-
|
156 |
-
mol_e = self.v_ma(mol_adj).view(-1,m,m, self.heads, c//self.heads)
|
157 |
-
prot_e = self.k_pa(prot_adj).view(-1,m,m, self.heads, c//self.heads)
|
158 |
-
|
159 |
-
query_mol_annot = query_mol_annot.unsqueeze(2)
|
160 |
-
key_prot_annot = key_prot_annot.unsqueeze(1)
|
161 |
-
|
162 |
-
|
163 |
-
|
164 |
-
#attn = torch.einsum('bnchd,bmahd->bnahd', query_mol_annot, key_prot_annot)
|
165 |
-
|
166 |
-
attn = query_mol_annot * key_prot_annot
|
167 |
-
|
168 |
-
attn = attn/ math.sqrt(self.dim)
|
169 |
-
|
170 |
-
|
171 |
-
attn = attn * (prot_e + 1) * mol_e
|
172 |
-
|
173 |
-
mol_e_new = attn.flatten(3)
|
174 |
-
|
175 |
-
mol_adj = self.out_ed(mol_e_new)
|
176 |
-
|
177 |
-
attn = F.softmax(attn, dim=2)
|
178 |
-
|
179 |
-
value_mol_annot = value_mol_annot.unsqueeze(1)
|
180 |
-
|
181 |
-
value_mol_annot = attn * value_mol_annot
|
182 |
-
|
183 |
-
value_mol_annot = value_mol_annot.sum(dim=2).flatten(2)
|
184 |
-
|
185 |
-
mol_annot = self.out_nd(value_mol_annot)
|
186 |
-
|
187 |
-
return mol_annot, prot_annot, mol_adj, prot_adj
|
188 |
-
|
189 |
-
class Decoder_Block(nn.Module):
|
190 |
-
def __init__(self, dim, heads, mlp_ratio=4, drop_rate=0.):
|
191 |
-
super().__init__()
|
192 |
-
|
193 |
-
|
194 |
-
self.ln1_ma = nn.LayerNorm(dim)
|
195 |
-
self.ln1_pa = nn.LayerNorm(dim)
|
196 |
-
self.ln1_mx = nn.LayerNorm(dim)
|
197 |
-
self.ln1_px = nn.LayerNorm(dim)
|
198 |
-
|
199 |
-
self.attn2 = Attention_new(dim, heads, drop_rate)
|
200 |
-
|
201 |
-
self.ln2_pa = nn.LayerNorm(dim)
|
202 |
-
self.ln2_px = nn.LayerNorm(dim)
|
203 |
-
|
204 |
-
self.dec_attn = enc_dec_attention(dim, heads, drop_rate, drop_rate)
|
205 |
-
|
206 |
-
self.ln3_ma = nn.LayerNorm(dim)
|
207 |
-
self.ln3_mx = nn.LayerNorm(dim)
|
208 |
-
|
209 |
-
self.mlp_ma = MLP(dim, dim, dropout=drop_rate)
|
210 |
-
self.mlp_mx = MLP(dim, dim, dropout=drop_rate)
|
211 |
-
|
212 |
-
self.ln4_ma = nn.LayerNorm(dim)
|
213 |
-
self.ln4_mx = nn.LayerNorm(dim)
|
214 |
-
|
215 |
-
|
216 |
-
def forward(self,mol_annot, prot_annot, mol_adj, prot_adj):
|
217 |
-
|
218 |
-
mol_annot = self.ln1_mx(mol_annot)
|
219 |
-
mol_adj = self.ln1_ma(mol_adj)
|
220 |
-
|
221 |
-
prot_annot = self.ln1_px(prot_annot)
|
222 |
-
prot_adj = self.ln1_pa(prot_adj)
|
223 |
-
|
224 |
-
px1, pa1= self.attn2(prot_annot, prot_adj)
|
225 |
-
|
226 |
-
prot_annot = prot_annot + px1
|
227 |
-
prot_adj = prot_adj + pa1
|
228 |
-
|
229 |
-
prot_annot = self.ln2_px(prot_annot)
|
230 |
-
prot_adj = self.ln2_pa(prot_adj)
|
231 |
-
|
232 |
-
mx1, prot_annot, ma1, prot_adj = self.dec_attn(mol_annot,prot_annot,mol_adj,prot_adj)
|
233 |
-
|
234 |
-
ma1 = mol_adj + ma1
|
235 |
-
mx1 = mol_annot + mx1
|
236 |
-
|
237 |
-
ma2 = self.ln3_ma(ma1)
|
238 |
-
mx2 = self.ln3_mx(mx1)
|
239 |
-
|
240 |
-
ma3 = self.mlp_ma(ma2)
|
241 |
-
mx3 = self.mlp_mx(mx2)
|
242 |
-
|
243 |
-
ma = ma3 + ma2
|
244 |
-
mx = mx3 + mx2
|
245 |
-
|
246 |
-
mol_adj = self.ln4_ma(ma)
|
247 |
-
mol_annot = self.ln4_mx(mx)
|
248 |
-
|
249 |
-
return mol_annot, prot_annot, mol_adj, prot_adj
|
250 |
-
|
251 |
-
class TransformerDecoder(nn.Module):
|
252 |
-
def __init__(self, dim, depth, heads, mlp_ratio=4, drop_rate=0.):
|
253 |
-
super().__init__()
|
254 |
-
|
255 |
-
self.Decoder_Blocks = nn.ModuleList([
|
256 |
-
Decoder_Block(dim, heads, mlp_ratio, drop_rate)
|
257 |
-
for i in range(depth)])
|
258 |
-
|
259 |
-
def forward(self, mol_annot, prot_annot, mol_adj, prot_adj):
|
260 |
-
|
261 |
-
for Decoder_Block in self.Decoder_Blocks:
|
262 |
-
mol_annot, prot_annot, mol_adj, prot_adj = Decoder_Block(mol_annot, prot_annot, mol_adj, prot_adj)
|
263 |
-
|
264 |
-
return mol_annot, prot_annot,mol_adj, prot_adj
|
265 |
-
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loss.py
DELETED
@@ -1,158 +0,0 @@
|
|
1 |
-
|
2 |
-
import torch
|
3 |
-
|
4 |
-
def discriminator_loss(generator, discriminator, mol_graph, adj, annot, batch_size, device, grad_pen, lambda_gp,z_edge,z_node):
|
5 |
-
|
6 |
-
# Compute loss with real molecules.
|
7 |
-
|
8 |
-
logits_real_disc = discriminator(mol_graph)
|
9 |
-
|
10 |
-
prediction_real = - torch.mean(logits_real_disc)
|
11 |
-
|
12 |
-
# Compute loss with fake molecules.
|
13 |
-
|
14 |
-
node, edge, node_sample, edge_sample = generator(z_edge, z_node)
|
15 |
-
|
16 |
-
graph = torch.cat((node_sample.view(batch_size, -1), edge_sample.view(batch_size, -1)), dim=-1)
|
17 |
-
|
18 |
-
logits_fake_disc = discriminator(graph.detach())
|
19 |
-
|
20 |
-
prediction_fake = torch.mean(logits_fake_disc)
|
21 |
-
|
22 |
-
# Compute gradient loss.
|
23 |
-
|
24 |
-
eps = torch.rand(mol_graph.size(0),1).to(device)
|
25 |
-
x_int0 = (eps * mol_graph + (1. - eps) * graph).requires_grad_(True)
|
26 |
-
|
27 |
-
grad0 = discriminator(x_int0)
|
28 |
-
d_loss_gp = grad_pen(grad0, x_int0)
|
29 |
-
|
30 |
-
# Calculate total loss
|
31 |
-
|
32 |
-
d_loss = prediction_fake + prediction_real + d_loss_gp * lambda_gp
|
33 |
-
|
34 |
-
return node, edge,d_loss
|
35 |
-
|
36 |
-
|
37 |
-
def generator_loss(generator, discriminator, v, adj, annot, batch_size, penalty, matrices2mol, fps_r,submodel, dataset_name):
|
38 |
-
|
39 |
-
# Compute loss with fake molecules.
|
40 |
-
|
41 |
-
node, edge, node_sample, edge_sample = generator(adj, annot)
|
42 |
-
|
43 |
-
|
44 |
-
graph = torch.cat((node_sample.view(batch_size, -1), edge_sample.view(batch_size, -1)), dim=-1)
|
45 |
-
|
46 |
-
|
47 |
-
logits_fake_disc = discriminator(graph)
|
48 |
-
|
49 |
-
prediction_fake = - torch.mean(logits_fake_disc)
|
50 |
-
|
51 |
-
# Produce molecules.
|
52 |
-
|
53 |
-
g_edges_hat_sample = torch.max(edge_sample, -1)[1]
|
54 |
-
g_nodes_hat_sample = torch.max(node_sample , -1)[1]
|
55 |
-
|
56 |
-
fake_mol = [matrices2mol(n_.data.cpu().numpy(), e_.data.cpu().numpy(), strict=True, file_name=dataset_name)
|
57 |
-
for e_, n_ in zip(g_edges_hat_sample, g_nodes_hat_sample)]
|
58 |
-
g_loss = prediction_fake
|
59 |
-
# Compute penalty loss.
|
60 |
-
if submodel == "RL":
|
61 |
-
reward = penalty(fake_mol, fps_r)
|
62 |
-
|
63 |
-
# Reinforcement Loss
|
64 |
-
|
65 |
-
rew_fake = v(graph)
|
66 |
-
|
67 |
-
reward_loss = torch.mean(rew_fake) ** 2 + reward
|
68 |
-
|
69 |
-
# Calculate total loss
|
70 |
-
|
71 |
-
g_loss = prediction_fake + reward_loss * 1
|
72 |
-
|
73 |
-
|
74 |
-
return g_loss, fake_mol, g_edges_hat_sample, g_nodes_hat_sample, node, edge
|
75 |
-
|
76 |
-
def discriminator2_loss(generator, discriminator, mol_graph, adj, annot, batch_size, device, grad_pen, lambda_gp,akt1_adj,akt1_annot):
|
77 |
-
|
78 |
-
# Generate molecules.
|
79 |
-
|
80 |
-
dr_edges, dr_nodes = generator(adj,
|
81 |
-
annot,
|
82 |
-
akt1_adj,
|
83 |
-
akt1_annot)
|
84 |
-
|
85 |
-
|
86 |
-
dr_edges_hat = dr_edges.view(batch_size, -1)
|
87 |
-
|
88 |
-
dr_nodes_hat = dr_nodes.view(batch_size, -1)
|
89 |
-
|
90 |
-
dr_graph = torch.cat((dr_nodes_hat, dr_edges_hat), dim=-1)
|
91 |
-
|
92 |
-
# Compute loss with fake molecules.
|
93 |
-
|
94 |
-
dr_logits_fake = discriminator(dr_graph.detach())
|
95 |
-
|
96 |
-
d2_loss_fake = torch.mean(dr_logits_fake)
|
97 |
-
|
98 |
-
# Compute loss with real molecules.
|
99 |
-
|
100 |
-
dr_logits_real2 = discriminator(mol_graph)
|
101 |
-
|
102 |
-
d2_loss_real = - torch.mean(dr_logits_real2)
|
103 |
-
|
104 |
-
# Compute gradient loss.
|
105 |
-
|
106 |
-
eps_dr = torch.rand(mol_graph.size(0),1).to(device)
|
107 |
-
x_int0_dr = (eps_dr * mol_graph + (1. - eps_dr) * dr_graph).requires_grad_(True)
|
108 |
-
|
109 |
-
|
110 |
-
grad0_dr = discriminator(x_int0_dr)
|
111 |
-
d2_loss_gp = grad_pen(grad0_dr, x_int0_dr)
|
112 |
-
|
113 |
-
# Compute total loss.
|
114 |
-
|
115 |
-
d2_loss = d2_loss_fake + d2_loss_real + d2_loss_gp * lambda_gp
|
116 |
-
|
117 |
-
return d2_loss
|
118 |
-
|
119 |
-
def generator2_loss(generator, discriminator, v, adj, annot, batch_size, penalty, matrices2mol, fps_r,ak1_adj,akt1_annot, submodel, drugs_name):
|
120 |
-
|
121 |
-
# Generate molecules.
|
122 |
-
|
123 |
-
dr_edges_g, dr_nodes_g = generator(adj,
|
124 |
-
annot,
|
125 |
-
ak1_adj,
|
126 |
-
akt1_annot)
|
127 |
-
|
128 |
-
dr_edges_hat_g = dr_edges_g.view(batch_size, -1)
|
129 |
-
|
130 |
-
dr_nodes_hat_g = dr_nodes_g.view(batch_size, -1)
|
131 |
-
|
132 |
-
dr_graph_g = torch.cat((dr_nodes_hat_g, dr_edges_hat_g), dim=-1)
|
133 |
-
|
134 |
-
# Compute loss with fake molecules.
|
135 |
-
|
136 |
-
dr_g_edges_hat_sample, dr_g_nodes_hat_sample = torch.max(dr_edges_g, -1)[1], torch.max(dr_nodes_g, -1)[1]
|
137 |
-
|
138 |
-
g_tra_logits_fake2 = discriminator(dr_graph_g)
|
139 |
-
|
140 |
-
g2_loss_fake = - torch.mean(g_tra_logits_fake2)
|
141 |
-
|
142 |
-
# Reward
|
143 |
-
fake_mol_g = [matrices2mol(n_.data.cpu().numpy(), e_.data.cpu().numpy(), strict=True, file_name=drugs_name)
|
144 |
-
for e_, n_ in zip(dr_g_edges_hat_sample, dr_g_nodes_hat_sample)]
|
145 |
-
g2_loss = g2_loss_fake
|
146 |
-
if submodel == "RL":
|
147 |
-
reward2 = penalty(fake_mol_g, fps_r)
|
148 |
-
|
149 |
-
# Reinforcement Loss
|
150 |
-
|
151 |
-
rew_fake2 = v(dr_graph_g)
|
152 |
-
reward_loss2 = torch.mean(rew_fake2) ** 2 + reward2
|
153 |
-
|
154 |
-
# Calculate total loss
|
155 |
-
|
156 |
-
g2_loss = g2_loss_fake + reward_loss2 * 10
|
157 |
-
|
158 |
-
return g2_loss, fake_mol_g, dr_g_edges_hat_sample, dr_g_nodes_hat_sample#, reward2
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models.py
DELETED
@@ -1,210 +0,0 @@
|
|
1 |
-
import torch
|
2 |
-
import torch.nn as nn
|
3 |
-
import torch.nn.functional as F
|
4 |
-
from layers import TransformerEncoder, TransformerDecoder
|
5 |
-
|
6 |
-
class Generator(nn.Module):
|
7 |
-
"""Generator network."""
|
8 |
-
def __init__(self, z_dim, act, vertexes, edges, nodes, dropout, dim, depth, heads, mlp_ratio, submodel):
|
9 |
-
super(Generator, self).__init__()
|
10 |
-
|
11 |
-
self.submodel = submodel
|
12 |
-
self.vertexes = vertexes
|
13 |
-
self.edges = edges
|
14 |
-
self.nodes = nodes
|
15 |
-
self.depth = depth
|
16 |
-
self.dim = dim
|
17 |
-
self.heads = heads
|
18 |
-
self.mlp_ratio = mlp_ratio
|
19 |
-
|
20 |
-
self.dropout = dropout
|
21 |
-
self.z_dim = z_dim
|
22 |
-
|
23 |
-
if act == "relu":
|
24 |
-
act = nn.ReLU()
|
25 |
-
elif act == "leaky":
|
26 |
-
act = nn.LeakyReLU()
|
27 |
-
elif act == "sigmoid":
|
28 |
-
act = nn.Sigmoid()
|
29 |
-
elif act == "tanh":
|
30 |
-
act = nn.Tanh()
|
31 |
-
self.features = vertexes * vertexes * edges + vertexes * nodes
|
32 |
-
self.transformer_dim = vertexes * vertexes * dim + vertexes * dim
|
33 |
-
self.pos_enc_dim = 5
|
34 |
-
#self.pos_enc = nn.Linear(self.pos_enc_dim, self.dim)
|
35 |
-
|
36 |
-
self.node_layers = nn.Sequential(nn.Linear(nodes, 64), act, nn.Linear(64,dim), act, nn.Dropout(self.dropout))
|
37 |
-
self.edge_layers = nn.Sequential(nn.Linear(edges, 64), act, nn.Linear(64,dim), act, nn.Dropout(self.dropout))
|
38 |
-
|
39 |
-
self.TransformerEncoder = TransformerEncoder(dim=self.dim, depth=self.depth, heads=self.heads, act = act,
|
40 |
-
mlp_ratio=self.mlp_ratio, drop_rate=self.dropout)
|
41 |
-
|
42 |
-
self.readout_e = nn.Linear(self.dim, edges)
|
43 |
-
self.readout_n = nn.Linear(self.dim, nodes)
|
44 |
-
self.softmax = nn.Softmax(dim = -1)
|
45 |
-
|
46 |
-
def _generate_square_subsequent_mask(self, sz):
|
47 |
-
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
|
48 |
-
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
|
49 |
-
return mask
|
50 |
-
|
51 |
-
def laplacian_positional_enc(self, adj):
|
52 |
-
|
53 |
-
A = adj
|
54 |
-
D = torch.diag(torch.count_nonzero(A, dim=-1))
|
55 |
-
L = torch.eye(A.shape[0], device=A.device) - D * A * D
|
56 |
-
|
57 |
-
EigVal, EigVec = torch.linalg.eig(L)
|
58 |
-
|
59 |
-
idx = torch.argsort(torch.real(EigVal))
|
60 |
-
EigVal, EigVec = EigVal[idx], torch.real(EigVec[:,idx])
|
61 |
-
pos_enc = EigVec[:,1:self.pos_enc_dim + 1]
|
62 |
-
|
63 |
-
return pos_enc
|
64 |
-
|
65 |
-
def forward(self, z_e, z_n):
|
66 |
-
b, n, c = z_n.shape
|
67 |
-
_, _, _ , d = z_e.shape
|
68 |
-
#random_mask_e = torch.randint(low=0,high=2,size=(b,n,n,d)).to(z_e.device).float()
|
69 |
-
#random_mask_n = torch.randint(low=0,high=2,size=(b,n,c)).to(z_n.device).float()
|
70 |
-
#z_e = F.relu(z_e - random_mask_e)
|
71 |
-
#z_n = F.relu(z_n - random_mask_n)
|
72 |
-
|
73 |
-
#mask = self._generate_square_subsequent_mask(self.vertexes).to(z_e.device)
|
74 |
-
|
75 |
-
node = self.node_layers(z_n)
|
76 |
-
|
77 |
-
edge = self.edge_layers(z_e)
|
78 |
-
|
79 |
-
edge = (edge + edge.permute(0,2,1,3))/2
|
80 |
-
|
81 |
-
#lap = [self.laplacian_positional_enc(torch.max(x,-1)[1]) for x in edge]
|
82 |
-
|
83 |
-
#lap = torch.stack(lap).to(node.device)
|
84 |
-
|
85 |
-
#pos_enc = self.pos_enc(lap)
|
86 |
-
|
87 |
-
#node = node + pos_enc
|
88 |
-
|
89 |
-
node, edge = self.TransformerEncoder(node,edge)
|
90 |
-
|
91 |
-
node_sample = self.softmax(self.readout_n(node))
|
92 |
-
|
93 |
-
edge_sample = self.softmax(self.readout_e(edge))
|
94 |
-
|
95 |
-
return node, edge, node_sample, edge_sample
|
96 |
-
|
97 |
-
|
98 |
-
|
99 |
-
class Generator2(nn.Module):
|
100 |
-
def __init__(self, dim, dec_dim, depth, heads, mlp_ratio, drop_rate, drugs_m_dim, drugs_b_dim, submodel):
|
101 |
-
super().__init__()
|
102 |
-
self.submodel = submodel
|
103 |
-
self.depth = depth
|
104 |
-
self.dim = dim
|
105 |
-
self.mlp_ratio = mlp_ratio
|
106 |
-
self.heads = heads
|
107 |
-
self.dropout_rate = drop_rate
|
108 |
-
self.drugs_m_dim = drugs_m_dim
|
109 |
-
self.drugs_b_dim = drugs_b_dim
|
110 |
-
|
111 |
-
self.pos_enc_dim = 5
|
112 |
-
|
113 |
-
|
114 |
-
if self.submodel == "Prot":
|
115 |
-
self.prot_n = torch.nn.Linear(3822, 45) ## exact dimension of protein features
|
116 |
-
self.prot_e = torch.nn.Linear(298116, 2025) ## exact dimension of protein features
|
117 |
-
|
118 |
-
self.protn_dim = torch.nn.Linear(1, dec_dim)
|
119 |
-
self.prote_dim = torch.nn.Linear(1, dec_dim)
|
120 |
-
|
121 |
-
|
122 |
-
self.mol_nodes = nn.Linear(dim, dec_dim)
|
123 |
-
self.mol_edges = nn.Linear(dim, dec_dim)
|
124 |
-
|
125 |
-
self.drug_nodes = nn.Linear(self.drugs_m_dim, dec_dim)
|
126 |
-
self.drug_edges = nn.Linear(self.drugs_b_dim, dec_dim)
|
127 |
-
|
128 |
-
self.TransformerDecoder = TransformerDecoder(dec_dim, depth, heads, mlp_ratio, drop_rate=self.dropout_rate)
|
129 |
-
|
130 |
-
self.nodes_output_layer = nn.Linear(dec_dim, self.drugs_m_dim)
|
131 |
-
self.edges_output_layer = nn.Linear(dec_dim, self.drugs_b_dim)
|
132 |
-
self.softmax = nn.Softmax(dim=-1)
|
133 |
-
|
134 |
-
def laplacian_positional_enc(self, adj):
|
135 |
-
|
136 |
-
A = adj
|
137 |
-
D = torch.diag(torch.count_nonzero(A, dim=-1))
|
138 |
-
L = torch.eye(A.shape[0], device=A.device) - D * A * D
|
139 |
-
|
140 |
-
EigVal, EigVec = torch.linalg.eig(L)
|
141 |
-
|
142 |
-
idx = torch.argsort(torch.real(EigVal))
|
143 |
-
EigVal, EigVec = EigVal[idx], torch.real(EigVec[:,idx])
|
144 |
-
pos_enc = EigVec[:,1:self.pos_enc_dim + 1]
|
145 |
-
|
146 |
-
return pos_enc
|
147 |
-
|
148 |
-
def _generate_square_subsequent_mask(self, sz):
|
149 |
-
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
|
150 |
-
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
|
151 |
-
return mask
|
152 |
-
|
153 |
-
def forward(self, edges_logits, nodes_logits ,akt1_adj,akt1_annot):
|
154 |
-
|
155 |
-
edges_logits = self.mol_edges(edges_logits)
|
156 |
-
nodes_logits = self.mol_nodes(nodes_logits)
|
157 |
-
|
158 |
-
if self.submodel != "Prot":
|
159 |
-
akt1_annot = self.drug_nodes(akt1_annot)
|
160 |
-
akt1_adj = self.drug_edges(akt1_adj)
|
161 |
-
|
162 |
-
else:
|
163 |
-
akt1_adj = self.prote_dim(self.prot_e(akt1_adj).view(1,45,45,1))
|
164 |
-
akt1_annot = self.protn_dim(self.prot_n(akt1_annot).view(1,45,1))
|
165 |
-
|
166 |
-
|
167 |
-
#lap = [self.laplacian_positional_enc(torch.max(x,-1)[1]) for x in drug_e]
|
168 |
-
#lap = torch.stack(lap).to(drug_e.device)
|
169 |
-
#pos_enc = self.pos_enc(lap)
|
170 |
-
#drug_n = drug_n + pos_enc
|
171 |
-
|
172 |
-
if self.submodel == "Ligand" or self.submodel == "RL" :
|
173 |
-
nodes_logits,akt1_annot, edges_logits, akt1_adj = self.TransformerDecoder(akt1_annot,nodes_logits,akt1_adj,edges_logits)
|
174 |
-
|
175 |
-
else:
|
176 |
-
nodes_logits,akt1_annot, edges_logits, akt1_adj = self.TransformerDecoder(nodes_logits,akt1_annot,edges_logits,akt1_adj)
|
177 |
-
|
178 |
-
edges_logits = self.edges_output_layer(edges_logits)
|
179 |
-
nodes_logits = self.nodes_output_layer(nodes_logits)
|
180 |
-
|
181 |
-
edges_logits = self.softmax(edges_logits)
|
182 |
-
nodes_logits = self.softmax(nodes_logits)
|
183 |
-
|
184 |
-
return edges_logits, nodes_logits
|
185 |
-
|
186 |
-
|
187 |
-
class simple_disc(nn.Module):
|
188 |
-
def __init__(self, act, m_dim, vertexes, b_dim):
|
189 |
-
super().__init__()
|
190 |
-
if act == "relu":
|
191 |
-
act = nn.ReLU()
|
192 |
-
elif act == "leaky":
|
193 |
-
act = nn.LeakyReLU()
|
194 |
-
elif act == "sigmoid":
|
195 |
-
act = nn.Sigmoid()
|
196 |
-
elif act == "tanh":
|
197 |
-
act = nn.Tanh()
|
198 |
-
features = vertexes * m_dim + vertexes * vertexes * b_dim
|
199 |
-
|
200 |
-
self.predictor = nn.Sequential(nn.Linear(features,256), act, nn.Linear(256,128), act, nn.Linear(128,64), act,
|
201 |
-
nn.Linear(64,32), act, nn.Linear(32,16), act,
|
202 |
-
nn.Linear(16,1))
|
203 |
-
|
204 |
-
def forward(self, x):
|
205 |
-
|
206 |
-
prediction = self.predictor(x)
|
207 |
-
|
208 |
-
#prediction = F.softmax(prediction,dim=-1)
|
209 |
-
|
210 |
-
return prediction
|
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|
new_dataloader.py
DELETED
@@ -1,293 +0,0 @@
|
|
1 |
-
import pickle
|
2 |
-
import numpy as np
|
3 |
-
import torch
|
4 |
-
from rdkit import Chem
|
5 |
-
from torch_geometric.data import (Data, InMemoryDataset)
|
6 |
-
import os.path as osp
|
7 |
-
from tqdm import tqdm
|
8 |
-
import re
|
9 |
-
from rdkit import RDLogger
|
10 |
-
RDLogger.DisableLog('rdApp.*')
|
11 |
-
class DruggenDataset(InMemoryDataset):
|
12 |
-
|
13 |
-
def __init__(self, root, dataset_file, raw_files, max_atom, features, transform=None, pre_transform=None, pre_filter=None):
|
14 |
-
self.dataset_name = dataset_file.split(".")[0]
|
15 |
-
self.dataset_file = dataset_file
|
16 |
-
self.raw_files = raw_files
|
17 |
-
self.max_atom = max_atom
|
18 |
-
self.features = features
|
19 |
-
super().__init__(root, transform, pre_transform, pre_filter)
|
20 |
-
path = osp.join(self.processed_dir, dataset_file)
|
21 |
-
self.data, self.slices = torch.load(path)
|
22 |
-
self.root = root
|
23 |
-
|
24 |
-
|
25 |
-
@property
|
26 |
-
def processed_dir(self):
|
27 |
-
|
28 |
-
return self.root
|
29 |
-
|
30 |
-
@property
|
31 |
-
def raw_file_names(self):
|
32 |
-
return self.raw_files
|
33 |
-
|
34 |
-
@property
|
35 |
-
def processed_file_names(self):
|
36 |
-
return self.dataset_file
|
37 |
-
|
38 |
-
def _generate_encoders_decoders(self, data):
|
39 |
-
|
40 |
-
self.data = data
|
41 |
-
print('Creating atoms encoder and decoder..')
|
42 |
-
atom_labels = sorted(set([atom.GetAtomicNum() for mol in self.data for atom in mol.GetAtoms()] + [0]))
|
43 |
-
self.atom_encoder_m = {l: i for i, l in enumerate(atom_labels)}
|
44 |
-
self.atom_decoder_m = {i: l for i, l in enumerate(atom_labels)}
|
45 |
-
self.atom_num_types = len(atom_labels)
|
46 |
-
print('Created atoms encoder and decoder with {} atom types and 1 PAD symbol!'.format(
|
47 |
-
self.atom_num_types - 1))
|
48 |
-
print("atom_labels", atom_labels)
|
49 |
-
print('Creating bonds encoder and decoder..')
|
50 |
-
bond_labels = [Chem.rdchem.BondType.ZERO] + list(sorted(set(bond.GetBondType()
|
51 |
-
for mol in self.data
|
52 |
-
for bond in mol.GetBonds())))
|
53 |
-
print("bond labels", bond_labels)
|
54 |
-
self.bond_encoder_m = {l: i for i, l in enumerate(bond_labels)}
|
55 |
-
self.bond_decoder_m = {i: l for i, l in enumerate(bond_labels)}
|
56 |
-
self.bond_num_types = len(bond_labels)
|
57 |
-
print('Created bonds encoder and decoder with {} bond types and 1 PAD symbol!'.format(
|
58 |
-
self.bond_num_types - 1))
|
59 |
-
#dataset_names = str(self.dataset_name)
|
60 |
-
with open("data/encoders/" +"atom_" + self.dataset_name + ".pkl","wb") as atom_encoders:
|
61 |
-
pickle.dump(self.atom_encoder_m,atom_encoders)
|
62 |
-
|
63 |
-
|
64 |
-
with open("data/decoders/" +"atom_" + self.dataset_name + ".pkl","wb") as atom_decoders:
|
65 |
-
pickle.dump(self.atom_decoder_m,atom_decoders)
|
66 |
-
|
67 |
-
|
68 |
-
with open("data/encoders/" +"bond_" + self.dataset_name + ".pkl","wb") as bond_encoders:
|
69 |
-
pickle.dump(self.bond_encoder_m,bond_encoders)
|
70 |
-
|
71 |
-
|
72 |
-
with open("data/decoders/" +"bond_" + self.dataset_name + ".pkl","wb") as bond_decoders:
|
73 |
-
pickle.dump(self.bond_decoder_m,bond_decoders)
|
74 |
-
|
75 |
-
|
76 |
-
|
77 |
-
def _genA(self, mol, connected=True, max_length=None):
|
78 |
-
|
79 |
-
max_length = max_length if max_length is not None else mol.GetNumAtoms()
|
80 |
-
|
81 |
-
A = np.zeros(shape=(max_length, max_length))
|
82 |
-
|
83 |
-
begin, end = [b.GetBeginAtomIdx() for b in mol.GetBonds()], [b.GetEndAtomIdx() for b in mol.GetBonds()]
|
84 |
-
bond_type = [self.bond_encoder_m[b.GetBondType()] for b in mol.GetBonds()]
|
85 |
-
|
86 |
-
A[begin, end] = bond_type
|
87 |
-
A[end, begin] = bond_type
|
88 |
-
|
89 |
-
degree = np.sum(A[:mol.GetNumAtoms(), :mol.GetNumAtoms()], axis=-1)
|
90 |
-
|
91 |
-
return A if connected and (degree > 0).all() else None
|
92 |
-
|
93 |
-
def _genX(self, mol, max_length=None):
|
94 |
-
|
95 |
-
max_length = max_length if max_length is not None else mol.GetNumAtoms()
|
96 |
-
|
97 |
-
return np.array([self.atom_encoder_m[atom.GetAtomicNum()] for atom in mol.GetAtoms()] + [0] * (
|
98 |
-
max_length - mol.GetNumAtoms()))
|
99 |
-
|
100 |
-
def _genF(self, mol, max_length=None):
|
101 |
-
|
102 |
-
max_length = max_length if max_length is not None else mol.GetNumAtoms()
|
103 |
-
|
104 |
-
features = np.array([[*[a.GetDegree() == i for i in range(5)],
|
105 |
-
*[a.GetExplicitValence() == i for i in range(9)],
|
106 |
-
*[int(a.GetHybridization()) == i for i in range(1, 7)],
|
107 |
-
*[a.GetImplicitValence() == i for i in range(9)],
|
108 |
-
a.GetIsAromatic(),
|
109 |
-
a.GetNoImplicit(),
|
110 |
-
*[a.GetNumExplicitHs() == i for i in range(5)],
|
111 |
-
*[a.GetNumImplicitHs() == i for i in range(5)],
|
112 |
-
*[a.GetNumRadicalElectrons() == i for i in range(5)],
|
113 |
-
a.IsInRing(),
|
114 |
-
*[a.IsInRingSize(i) for i in range(2, 9)]] for a in mol.GetAtoms()], dtype=np.int32)
|
115 |
-
|
116 |
-
return np.vstack((features, np.zeros((max_length - features.shape[0], features.shape[1]))))
|
117 |
-
|
118 |
-
def decoder_load(self, dictionary_name, file):
|
119 |
-
with open("data/decoders/" + dictionary_name + "_" + file + '.pkl', 'rb') as f:
|
120 |
-
return pickle.load(f)
|
121 |
-
|
122 |
-
def drugs_decoder_load(self, dictionary_name):
|
123 |
-
with open("data/decoders/" + dictionary_name +'.pkl', 'rb') as f:
|
124 |
-
return pickle.load(f)
|
125 |
-
|
126 |
-
def matrices2mol(self, node_labels, edge_labels, strict=True, file_name=None):
|
127 |
-
mol = Chem.RWMol()
|
128 |
-
RDLogger.DisableLog('rdApp.*')
|
129 |
-
atom_decoders = self.decoder_load("atom", file_name)
|
130 |
-
bond_decoders = self.decoder_load("bond", file_name)
|
131 |
-
|
132 |
-
for node_label in node_labels:
|
133 |
-
mol.AddAtom(Chem.Atom(atom_decoders[node_label]))
|
134 |
-
|
135 |
-
for start, end in zip(*np.nonzero(edge_labels)):
|
136 |
-
if start > end:
|
137 |
-
mol.AddBond(int(start), int(end), bond_decoders[edge_labels[start, end]])
|
138 |
-
#mol = self.correct_mol(mol)
|
139 |
-
if strict:
|
140 |
-
try:
|
141 |
-
|
142 |
-
Chem.SanitizeMol(mol)
|
143 |
-
except:
|
144 |
-
mol = None
|
145 |
-
|
146 |
-
return mol
|
147 |
-
|
148 |
-
def drug_decoder_load(self, dictionary_name, file):
|
149 |
-
|
150 |
-
''' Loading the atom and bond decoders '''
|
151 |
-
|
152 |
-
with open("data/decoders/" + dictionary_name +"_" + file +'.pkl', 'rb') as f:
|
153 |
-
|
154 |
-
return pickle.load(f)
|
155 |
-
def matrices2mol_drugs(self, node_labels, edge_labels, strict=True, file_name=None):
|
156 |
-
mol = Chem.RWMol()
|
157 |
-
RDLogger.DisableLog('rdApp.*')
|
158 |
-
atom_decoders = self.drug_decoder_load("atom", file_name)
|
159 |
-
bond_decoders = self.drug_decoder_load("bond", file_name)
|
160 |
-
|
161 |
-
for node_label in node_labels:
|
162 |
-
|
163 |
-
mol.AddAtom(Chem.Atom(atom_decoders[node_label]))
|
164 |
-
|
165 |
-
for start, end in zip(*np.nonzero(edge_labels)):
|
166 |
-
if start > end:
|
167 |
-
mol.AddBond(int(start), int(end), bond_decoders[edge_labels[start, end]])
|
168 |
-
#mol = self.correct_mol(mol)
|
169 |
-
if strict:
|
170 |
-
try:
|
171 |
-
Chem.SanitizeMol(mol)
|
172 |
-
except:
|
173 |
-
mol = None
|
174 |
-
|
175 |
-
return mol
|
176 |
-
def check_valency(self,mol):
|
177 |
-
"""
|
178 |
-
Checks that no atoms in the mol have exceeded their possible
|
179 |
-
valency
|
180 |
-
:return: True if no valency issues, False otherwise
|
181 |
-
"""
|
182 |
-
try:
|
183 |
-
Chem.SanitizeMol(mol, sanitizeOps=Chem.SanitizeFlags.SANITIZE_PROPERTIES)
|
184 |
-
return True, None
|
185 |
-
except ValueError as e:
|
186 |
-
e = str(e)
|
187 |
-
p = e.find('#')
|
188 |
-
e_sub = e[p:]
|
189 |
-
atomid_valence = list(map(int, re.findall(r'\d+', e_sub)))
|
190 |
-
return False, atomid_valence
|
191 |
-
|
192 |
-
|
193 |
-
def correct_mol(self,x):
|
194 |
-
xsm = Chem.MolToSmiles(x, isomericSmiles=True)
|
195 |
-
mol = x
|
196 |
-
while True:
|
197 |
-
flag, atomid_valence = self.check_valency(mol)
|
198 |
-
if flag:
|
199 |
-
break
|
200 |
-
else:
|
201 |
-
assert len (atomid_valence) == 2
|
202 |
-
idx = atomid_valence[0]
|
203 |
-
v = atomid_valence[1]
|
204 |
-
queue = []
|
205 |
-
for b in mol.GetAtomWithIdx(idx).GetBonds():
|
206 |
-
queue.append(
|
207 |
-
(b.GetIdx(), int(b.GetBondType()), b.GetBeginAtomIdx(), b.GetEndAtomIdx())
|
208 |
-
)
|
209 |
-
queue.sort(key=lambda tup: tup[1], reverse=True)
|
210 |
-
if len(queue) > 0:
|
211 |
-
start = queue[0][2]
|
212 |
-
end = queue[0][3]
|
213 |
-
t = queue[0][1] - 1
|
214 |
-
mol.RemoveBond(start, end)
|
215 |
-
|
216 |
-
#if t >= 1:
|
217 |
-
|
218 |
-
#mol.AddBond(start, end, self.decoder_load('bond_decoders')[t])
|
219 |
-
# if '.' in Chem.MolToSmiles(mol, isomericSmiles=True):
|
220 |
-
# mol.AddBond(start, end, self.decoder_load('bond_decoders')[t])
|
221 |
-
# print(tt)
|
222 |
-
# print(Chem.MolToSmiles(mol, isomericSmiles=True))
|
223 |
-
|
224 |
-
return mol
|
225 |
-
|
226 |
-
|
227 |
-
|
228 |
-
def label2onehot(self, labels, dim):
|
229 |
-
|
230 |
-
"""Convert label indices to one-hot vectors."""
|
231 |
-
|
232 |
-
out = torch.zeros(list(labels.size())+[dim])
|
233 |
-
out.scatter_(len(out.size())-1,labels.unsqueeze(-1),1.)
|
234 |
-
|
235 |
-
return out.float()
|
236 |
-
|
237 |
-
def process(self, size= None):
|
238 |
-
|
239 |
-
mols = [Chem.MolFromSmiles(line) for line in open(self.raw_files, 'r').readlines()]
|
240 |
-
|
241 |
-
mols = list(filter(lambda x: x.GetNumAtoms() <= self.max_atom, mols))
|
242 |
-
mols = mols[:size]
|
243 |
-
indices = range(len(mols))
|
244 |
-
|
245 |
-
self._generate_encoders_decoders(mols)
|
246 |
-
|
247 |
-
|
248 |
-
|
249 |
-
pbar = tqdm(total=len(indices))
|
250 |
-
pbar.set_description(f'Processing chembl dataset')
|
251 |
-
max_length = max(mol.GetNumAtoms() for mol in mols)
|
252 |
-
data_list = []
|
253 |
-
|
254 |
-
self.m_dim = len(self.atom_decoder_m)
|
255 |
-
for idx in indices:
|
256 |
-
mol = mols[idx]
|
257 |
-
A = self._genA(mol, connected=True, max_length=max_length)
|
258 |
-
if A is not None:
|
259 |
-
|
260 |
-
|
261 |
-
x = torch.from_numpy(self._genX(mol, max_length=max_length)).to(torch.long).view(1, -1)
|
262 |
-
|
263 |
-
x = self.label2onehot(x,self.m_dim).squeeze()
|
264 |
-
if self.features:
|
265 |
-
f = torch.from_numpy(self._genF(mol, max_length=max_length)).to(torch.long).view(x.shape[0], -1)
|
266 |
-
x = torch.concat((x,f), dim=-1)
|
267 |
-
|
268 |
-
adjacency = torch.from_numpy(A)
|
269 |
-
|
270 |
-
edge_index = adjacency.nonzero(as_tuple=False).t().contiguous()
|
271 |
-
edge_attr = adjacency[edge_index[0], edge_index[1]].to(torch.long)
|
272 |
-
|
273 |
-
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr)
|
274 |
-
|
275 |
-
if self.pre_filter is not None and not self.pre_filter(data):
|
276 |
-
continue
|
277 |
-
|
278 |
-
if self.pre_transform is not None:
|
279 |
-
data = self.pre_transform(data)
|
280 |
-
|
281 |
-
data_list.append(data)
|
282 |
-
pbar.update(1)
|
283 |
-
|
284 |
-
pbar.close()
|
285 |
-
|
286 |
-
torch.save(self.collate(data_list), osp.join(self.processed_dir, self.dataset_file))
|
287 |
-
|
288 |
-
|
289 |
-
|
290 |
-
|
291 |
-
if __name__ == '__main__':
|
292 |
-
data = DruggenDataset("data")
|
293 |
-
|
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|
packages.txt
DELETED
@@ -1 +0,0 @@
|
|
1 |
-
libcairo2-dev
|
|
|
|
requirements.txt
DELETED
@@ -1,12 +0,0 @@
|
|
1 |
-
torch
|
2 |
-
rdkit-pypi
|
3 |
-
tqdm
|
4 |
-
numpy
|
5 |
-
seaborn
|
6 |
-
matplotlib
|
7 |
-
pandas
|
8 |
-
torch_geometric
|
9 |
-
# demo related installs
|
10 |
-
streamlit
|
11 |
-
ipython
|
12 |
-
streamlit-ext
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
trainer.py
DELETED
@@ -1,927 +0,0 @@
|
|
1 |
-
import os
|
2 |
-
import time
|
3 |
-
import torch.nn
|
4 |
-
import torch
|
5 |
-
|
6 |
-
from utils import *
|
7 |
-
from models import Generator, Generator2, simple_disc
|
8 |
-
import torch_geometric.utils as geoutils
|
9 |
-
#import wandb
|
10 |
-
import re
|
11 |
-
from torch_geometric.loader import DataLoader
|
12 |
-
from new_dataloader import DruggenDataset
|
13 |
-
import torch.utils.data
|
14 |
-
from rdkit import RDLogger
|
15 |
-
import pickle
|
16 |
-
from rdkit.Chem.Scaffolds import MurckoScaffold
|
17 |
-
torch.set_num_threads(5)
|
18 |
-
RDLogger.DisableLog('rdApp.*')
|
19 |
-
from loss import discriminator_loss, generator_loss, discriminator2_loss, generator2_loss
|
20 |
-
from training_data import load_data
|
21 |
-
import random
|
22 |
-
from tqdm import tqdm
|
23 |
-
|
24 |
-
class Trainer(object):
|
25 |
-
|
26 |
-
"""Trainer for training and testing DrugGEN."""
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27 |
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|
28 |
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def __init__(self, config):
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29 |
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30 |
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if config.set_seed:
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31 |
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np.random.seed(config.seed)
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32 |
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random.seed(config.seed)
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33 |
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torch.manual_seed(config.seed)
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34 |
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torch.cuda.manual_seed(config.seed)
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35 |
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36 |
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torch.backends.cudnn.deterministic = True
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37 |
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torch.backends.cudnn.benchmark = False
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38 |
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|
39 |
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os.environ["PYTHONHASHSEED"] = str(config.seed)
|
40 |
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|
41 |
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print(f'Using seed {config.seed}')
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42 |
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|
43 |
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self.device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
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44 |
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"""Initialize configurations."""
|
45 |
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self.submodel = config.submodel
|
46 |
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self.inference_model = config.inference_model
|
47 |
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# Data loader.
|
48 |
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self.raw_file = config.raw_file # SMILES containing text file for first dataset.
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49 |
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# Write the full path to file.
|
50 |
-
|
51 |
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self.drug_raw_file = config.drug_raw_file # SMILES containing text file for second dataset.
|
52 |
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# Write the full path to file.
|
53 |
-
|
54 |
-
|
55 |
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self.dataset_file = config.dataset_file # Dataset file name for the first GAN.
|
56 |
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# Contains large number of molecules.
|
57 |
-
|
58 |
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self.drugs_dataset_file = config.drug_dataset_file # Drug dataset file name for the second GAN.
|
59 |
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# Contains drug molecules only. (In this case AKT1 inhibitors.)
|
60 |
-
|
61 |
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self.inf_raw_file = config.inf_raw_file # SMILES containing text file for first dataset.
|
62 |
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# Write the full path to file.
|
63 |
-
|
64 |
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self.inf_drug_raw_file = config.inf_drug_raw_file # SMILES containing text file for second dataset.
|
65 |
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# Write the full path to file.
|
66 |
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|
67 |
-
|
68 |
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self.inf_dataset_file = config.inf_dataset_file # Dataset file name for the first GAN.
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69 |
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# Contains large number of molecules.
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70 |
-
|
71 |
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self.inf_drugs_dataset_file = config.inf_drug_dataset_file # Drug dataset file name for the second GAN.
|
72 |
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# Contains drug molecules only. (In this case AKT1 inhibitors.)
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73 |
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self.inference_iterations = config.inference_iterations
|
74 |
-
|
75 |
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self.inf_batch_size = config.inf_batch_size
|
76 |
-
|
77 |
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self.mol_data_dir = config.mol_data_dir # Directory where the dataset files are stored.
|
78 |
-
|
79 |
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self.drug_data_dir = config.drug_data_dir # Directory where the drug dataset files are stored.
|
80 |
-
|
81 |
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self.dataset_name = self.dataset_file.split(".")[0]
|
82 |
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self.drugs_name = self.drugs_dataset_file.split(".")[0]
|
83 |
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|
84 |
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self.max_atom = config.max_atom # Model is based on one-shot generation.
|
85 |
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# Max atom number for molecules must be specified.
|
86 |
-
|
87 |
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self.features = config.features # Small model uses atom types as node features. (Boolean, False uses atom types only.)
|
88 |
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# Additional node features can be added. Please check new_dataloarder.py Line 102.
|
89 |
-
|
90 |
-
|
91 |
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self.batch_size = config.batch_size # Batch size for training.
|
92 |
-
|
93 |
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self.dataset = DruggenDataset(self.mol_data_dir,
|
94 |
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self.dataset_file,
|
95 |
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self.raw_file,
|
96 |
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self.max_atom,
|
97 |
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self.features) # Dataset for the first GAN. Custom dataset class from PyG parent class.
|
98 |
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# Can create any molecular graph dataset given smiles string.
|
99 |
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# Nonisomeric SMILES are suggested but not necessary.
|
100 |
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# Uses sparse matrix representation for graphs,
|
101 |
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# For computational and speed efficiency.
|
102 |
-
|
103 |
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self.loader = DataLoader(self.dataset,
|
104 |
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shuffle=True,
|
105 |
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batch_size=self.batch_size,
|
106 |
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drop_last=True) # PyG dataloader for the first GAN.
|
107 |
-
|
108 |
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self.drugs = DruggenDataset(self.drug_data_dir,
|
109 |
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self.drugs_dataset_file,
|
110 |
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self.drug_raw_file,
|
111 |
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self.max_atom,
|
112 |
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self.features) # Dataset for the second GAN. Custom dataset class from PyG parent class.
|
113 |
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# Can create any molecular graph dataset given smiles string.
|
114 |
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# Nonisomeric SMILES are suggested but not necessary.
|
115 |
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# Uses sparse matrix representation for graphs,
|
116 |
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# For computational and speed efficiency.
|
117 |
-
|
118 |
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self.drugs_loader = DataLoader(self.drugs,
|
119 |
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shuffle=True,
|
120 |
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batch_size=self.batch_size,
|
121 |
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drop_last=True) # PyG dataloader for the second GAN.
|
122 |
-
|
123 |
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# Atom and bond type dimensions for the construction of the model.
|
124 |
-
|
125 |
-
self.atom_decoders = self.decoder_load("atom") # Atom type decoders for first GAN.
|
126 |
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# eg. 0:0, 1:6 (C), 2:7 (N), 3:8 (O), 4:9 (F)
|
127 |
-
|
128 |
-
self.bond_decoders = self.decoder_load("bond") # Bond type decoders for first GAN.
|
129 |
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# eg. 0: (no-bond), 1: (single), 2: (double), 3: (triple), 4: (aromatic)
|
130 |
-
|
131 |
-
self.m_dim = len(self.atom_decoders) if not self.features else int(self.loader.dataset[0].x.shape[1]) # Atom type dimension.
|
132 |
-
|
133 |
-
self.b_dim = len(self.bond_decoders) # Bond type dimension.
|
134 |
-
|
135 |
-
self.vertexes = int(self.loader.dataset[0].x.shape[0]) # Number of nodes in the graph.
|
136 |
-
|
137 |
-
self.drugs_atom_decoders = self.drug_decoder_load("atom") # Atom type decoders for second GAN.
|
138 |
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# eg. 0:0, 1:6 (C), 2:7 (N), 3:8 (O), 4:9 (F)
|
139 |
-
|
140 |
-
self.drugs_bond_decoders = self.drug_decoder_load("bond") # Bond type decoders for second GAN.
|
141 |
-
# eg. 0: (no-bond), 1: (single), 2: (double), 3: (triple), 4: (aromatic)
|
142 |
-
|
143 |
-
self.drugs_m_dim = len(self.drugs_atom_decoders) if not self.features else int(self.drugs_loader.dataset[0].x.shape[1]) # Atom type dimension.
|
144 |
-
|
145 |
-
self.drugs_b_dim = len(self.drugs_bond_decoders) # Bond type dimension.
|
146 |
-
|
147 |
-
self.drug_vertexes = int(self.drugs_loader.dataset[0].x.shape[0]) # Number of nodes in the graph.
|
148 |
-
|
149 |
-
# Transformer and Convolution configurations.
|
150 |
-
|
151 |
-
self.act = config.act
|
152 |
-
|
153 |
-
self.z_dim = config.z_dim
|
154 |
-
|
155 |
-
self.lambda_gp = config.lambda_gp
|
156 |
-
|
157 |
-
self.dim = config.dim
|
158 |
-
|
159 |
-
self.depth = config.depth
|
160 |
-
|
161 |
-
self.heads = config.heads
|
162 |
-
|
163 |
-
self.mlp_ratio = config.mlp_ratio
|
164 |
-
|
165 |
-
self.dec_depth = config.dec_depth
|
166 |
-
|
167 |
-
self.dec_heads = config.dec_heads
|
168 |
-
|
169 |
-
self.dec_dim = config.dec_dim
|
170 |
-
|
171 |
-
self.dis_select = config.dis_select
|
172 |
-
|
173 |
-
"""self.la = config.la
|
174 |
-
self.la2 = config.la2
|
175 |
-
self.gcn_depth = config.gcn_depth
|
176 |
-
self.g_conv_dim = config.g_conv_dim
|
177 |
-
self.d_conv_dim = config.d_conv_dim"""
|
178 |
-
"""# PNA config
|
179 |
-
|
180 |
-
self.agg = config.aggregators
|
181 |
-
self.sca = config.scalers
|
182 |
-
self.pna_in_ch = config.pna_in_ch
|
183 |
-
self.pna_out_ch = config.pna_out_ch
|
184 |
-
self.edge_dim = config.edge_dim
|
185 |
-
self.towers = config.towers
|
186 |
-
self.pre_lay = config.pre_lay
|
187 |
-
self.post_lay = config.post_lay
|
188 |
-
self.pna_layer_num = config.pna_layer_num
|
189 |
-
self.graph_add = config.graph_add"""
|
190 |
-
|
191 |
-
# Training configurations.
|
192 |
-
|
193 |
-
self.epoch = config.epoch
|
194 |
-
|
195 |
-
self.g_lr = config.g_lr
|
196 |
-
|
197 |
-
self.d_lr = config.d_lr
|
198 |
-
|
199 |
-
self.g2_lr = config.g2_lr
|
200 |
-
|
201 |
-
self.d2_lr = config.d2_lr
|
202 |
-
|
203 |
-
self.dropout = config.dropout
|
204 |
-
|
205 |
-
self.dec_dropout = config.dec_dropout
|
206 |
-
|
207 |
-
self.n_critic = config.n_critic
|
208 |
-
|
209 |
-
self.beta1 = config.beta1
|
210 |
-
|
211 |
-
self.beta2 = config.beta2
|
212 |
-
|
213 |
-
self.resume_iters = config.resume_iters
|
214 |
-
|
215 |
-
self.warm_up_steps = config.warm_up_steps
|
216 |
-
|
217 |
-
# Test configurations.
|
218 |
-
|
219 |
-
self.num_test_epoch = config.num_test_epoch
|
220 |
-
|
221 |
-
self.test_iters = config.test_iters
|
222 |
-
|
223 |
-
self.inference_sample_num = config.inference_sample_num
|
224 |
-
|
225 |
-
# Directories.
|
226 |
-
|
227 |
-
self.log_dir = config.log_dir
|
228 |
-
self.sample_dir = config.sample_dir
|
229 |
-
self.model_save_dir = config.model_save_dir
|
230 |
-
self.result_dir = config.result_dir
|
231 |
-
|
232 |
-
# Step size.
|
233 |
-
|
234 |
-
self.log_step = config.log_sample_step
|
235 |
-
self.clipping_value = config.clipping_value
|
236 |
-
# Miscellaneous.
|
237 |
-
|
238 |
-
# resume training
|
239 |
-
|
240 |
-
self.resume = config.resume
|
241 |
-
self.resume_epoch = config.resume_epoch
|
242 |
-
self.resume_iter = config.resume_iter
|
243 |
-
self.resume_directory = config.resume_directory
|
244 |
-
|
245 |
-
|
246 |
-
self.mode = config.mode
|
247 |
-
|
248 |
-
self.noise_strength_0 = torch.nn.Parameter(torch.zeros([]))
|
249 |
-
self.noise_strength_1 = torch.nn.Parameter(torch.zeros([]))
|
250 |
-
self.noise_strength_2 = torch.nn.Parameter(torch.zeros([]))
|
251 |
-
self.noise_strength_3 = torch.nn.Parameter(torch.zeros([]))
|
252 |
-
|
253 |
-
self.init_type = config.init_type
|
254 |
-
self.build_model()
|
255 |
-
|
256 |
-
|
257 |
-
|
258 |
-
def build_model(self):
|
259 |
-
"""Create generators and discriminators."""
|
260 |
-
|
261 |
-
''' Generator is based on Transformer Encoder:
|
262 |
-
|
263 |
-
@ g_conv_dim: Dimensions for first MLP layers before Transformer Encoder
|
264 |
-
@ vertexes: maximum length of generated molecules (atom length)
|
265 |
-
@ b_dim: number of bond types
|
266 |
-
@ m_dim: number of atom types (or number of features used)
|
267 |
-
@ dropout: dropout possibility
|
268 |
-
@ dim: Hidden dimension of Transformer Encoder
|
269 |
-
@ depth: Transformer layer number
|
270 |
-
@ heads: Number of multihead-attention heads
|
271 |
-
@ mlp_ratio: Read-out layer dimension of Transformer
|
272 |
-
@ drop_rate: depricated
|
273 |
-
@ tra_conv: Whether module creates output for TransformerConv discriminator
|
274 |
-
'''
|
275 |
-
|
276 |
-
self.G = Generator(self.z_dim,
|
277 |
-
self.act,
|
278 |
-
self.vertexes,
|
279 |
-
self.b_dim,
|
280 |
-
self.m_dim,
|
281 |
-
self.dropout,
|
282 |
-
dim=self.dim,
|
283 |
-
depth=self.depth,
|
284 |
-
heads=self.heads,
|
285 |
-
mlp_ratio=self.mlp_ratio,
|
286 |
-
submodel = self.submodel)
|
287 |
-
|
288 |
-
self.G2 = Generator2(self.dim,
|
289 |
-
self.dec_dim,
|
290 |
-
self.dec_depth,
|
291 |
-
self.dec_heads,
|
292 |
-
self.mlp_ratio,
|
293 |
-
self.dec_dropout,
|
294 |
-
self.drugs_m_dim,
|
295 |
-
self.drugs_b_dim,
|
296 |
-
self.submodel)
|
297 |
-
|
298 |
-
|
299 |
-
|
300 |
-
''' Discriminator implementation with PNA:
|
301 |
-
|
302 |
-
@ deg: Degree distribution based on used data. (Created with _genDegree() function)
|
303 |
-
@ agg: aggregators used in PNA
|
304 |
-
@ sca: scalers used in PNA
|
305 |
-
@ pna_in_ch: First PNA hidden dimension
|
306 |
-
@ pna_out_ch: Last PNA hidden dimension
|
307 |
-
@ edge_dim: Edge hidden dimension
|
308 |
-
@ towers: Number of towers (Splitting the hidden dimension to multiple parallel processes)
|
309 |
-
@ pre_lay: Pre-transformation layer
|
310 |
-
@ post_lay: Post-transformation layer
|
311 |
-
@ pna_layer_num: number of PNA layers
|
312 |
-
@ graph_add: global pooling layer selection
|
313 |
-
'''
|
314 |
-
|
315 |
-
|
316 |
-
''' Discriminator implementation with Graph Convolution:
|
317 |
-
|
318 |
-
@ d_conv_dim: convolution dimensions for GCN
|
319 |
-
@ m_dim: number of atom types (or number of features used)
|
320 |
-
@ b_dim: number of bond types
|
321 |
-
@ dropout: dropout possibility
|
322 |
-
'''
|
323 |
-
|
324 |
-
''' Discriminator implementation with MLP:
|
325 |
-
|
326 |
-
@ act: Activation function for MLP
|
327 |
-
@ m_dim: number of atom types (or number of features used)
|
328 |
-
@ b_dim: number of bond types
|
329 |
-
@ dropout: dropout possibility
|
330 |
-
@ vertexes: maximum length of generated molecules (molecule length)
|
331 |
-
'''
|
332 |
-
|
333 |
-
#self.D = Discriminator_old(self.d_conv_dim, self.m_dim , self.b_dim, self.dropout, self.gcn_depth)
|
334 |
-
self.D2 = simple_disc("tanh", self.drugs_m_dim, self.drug_vertexes, self.drugs_b_dim)
|
335 |
-
self.D = simple_disc("tanh", self.m_dim, self.vertexes, self.b_dim)
|
336 |
-
self.V = simple_disc("tanh", self.m_dim, self.vertexes, self.b_dim)
|
337 |
-
self.V2 = simple_disc("tanh", self.drugs_m_dim, self.drug_vertexes, self.drugs_b_dim)
|
338 |
-
|
339 |
-
''' Optimizers for G1, G2, D1, and D2:
|
340 |
-
|
341 |
-
Adam Optimizer is used and different beta1 and beta2s are used for GAN1 and GAN2
|
342 |
-
'''
|
343 |
-
|
344 |
-
self.g_optimizer = torch.optim.AdamW(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])
|
345 |
-
self.g2_optimizer = torch.optim.AdamW(self.G2.parameters(), self.g2_lr, [self.beta1, self.beta2])
|
346 |
-
|
347 |
-
self.d_optimizer = torch.optim.AdamW(self.D.parameters(), self.d_lr, [self.beta1, self.beta2])
|
348 |
-
self.d2_optimizer = torch.optim.AdamW(self.D2.parameters(), self.d2_lr, [self.beta1, self.beta2])
|
349 |
-
|
350 |
-
|
351 |
-
|
352 |
-
self.v_optimizer = torch.optim.AdamW(self.V.parameters(), self.d_lr, [self.beta1, self.beta2])
|
353 |
-
self.v2_optimizer = torch.optim.AdamW(self.V2.parameters(), self.d2_lr, [self.beta1, self.beta2])
|
354 |
-
''' Learning rate scheduler:
|
355 |
-
|
356 |
-
Changes learning rate based on loss.
|
357 |
-
'''
|
358 |
-
|
359 |
-
#self.scheduler_g = ReduceLROnPlateau(self.g_optimizer, mode='min', factor=0.5, patience=10, min_lr=0.00001)
|
360 |
-
|
361 |
-
|
362 |
-
#self.scheduler_d = ReduceLROnPlateau(self.d_optimizer, mode='min', factor=0.5, patience=10, min_lr=0.00001)
|
363 |
-
|
364 |
-
#self.scheduler_v = ReduceLROnPlateau(self.v_optimizer, mode='min', factor=0.5, patience=10, min_lr=0.00001)
|
365 |
-
#self.scheduler_g2 = ReduceLROnPlateau(self.g2_optimizer, mode='min', factor=0.5, patience=10, min_lr=0.00001)
|
366 |
-
#self.scheduler_d2 = ReduceLROnPlateau(self.d2_optimizer, mode='min', factor=0.5, patience=10, min_lr=0.00001)
|
367 |
-
#self.scheduler_v2 = ReduceLROnPlateau(self.v2_optimizer, mode='min', factor=0.5, patience=10, min_lr=0.00001)
|
368 |
-
self.print_network(self.G, 'G')
|
369 |
-
self.print_network(self.D, 'D')
|
370 |
-
|
371 |
-
self.print_network(self.G2, 'G2')
|
372 |
-
self.print_network(self.D2, 'D2')
|
373 |
-
|
374 |
-
self.G.to(self.device)
|
375 |
-
self.D.to(self.device)
|
376 |
-
|
377 |
-
self.V.to(self.device)
|
378 |
-
self.V2.to(self.device)
|
379 |
-
self.G2.to(self.device)
|
380 |
-
self.D2.to(self.device)
|
381 |
-
|
382 |
-
#self.V2.to(self.device)
|
383 |
-
#self.modules_of_the_model = (self.G, self.D, self.G2, self.D2)
|
384 |
-
"""for p in self.G.parameters():
|
385 |
-
if p.dim() > 1:
|
386 |
-
if self.init_type == 'uniform':
|
387 |
-
torch.nn.init.xavier_uniform_(p)
|
388 |
-
elif self.init_type == 'normal':
|
389 |
-
torch.nn.init.xavier_normal_(p)
|
390 |
-
elif self.init_type == 'random_normal':
|
391 |
-
torch.nn.init.normal_(p, 0.0, 0.02)
|
392 |
-
for p in self.G2.parameters():
|
393 |
-
if p.dim() > 1:
|
394 |
-
if self.init_type == 'uniform':
|
395 |
-
torch.nn.init.xavier_uniform_(p)
|
396 |
-
elif self.init_type == 'normal':
|
397 |
-
torch.nn.init.xavier_normal_(p)
|
398 |
-
elif self.init_type == 'random_normal':
|
399 |
-
torch.nn.init.normal_(p, 0.0, 0.02)
|
400 |
-
if self.dis_select == "conv":
|
401 |
-
for p in self.D.parameters():
|
402 |
-
if p.dim() > 1:
|
403 |
-
if self.init_type == 'uniform':
|
404 |
-
torch.nn.init.xavier_uniform_(p)
|
405 |
-
elif self.init_type == 'normal':
|
406 |
-
torch.nn.init.xavier_normal_(p)
|
407 |
-
elif self.init_type == 'random_normal':
|
408 |
-
torch.nn.init.normal_(p, 0.0, 0.02)
|
409 |
-
|
410 |
-
if self.dis_select == "conv":
|
411 |
-
for p in self.D2.parameters():
|
412 |
-
if p.dim() > 1:
|
413 |
-
if self.init_type == 'uniform':
|
414 |
-
torch.nn.init.xavier_uniform_(p)
|
415 |
-
elif self.init_type == 'normal':
|
416 |
-
torch.nn.init.xavier_normal_(p)
|
417 |
-
elif self.init_type == 'random_normal':
|
418 |
-
torch.nn.init.normal_(p, 0.0, 0.02)"""
|
419 |
-
|
420 |
-
|
421 |
-
def decoder_load(self, dictionary_name):
|
422 |
-
|
423 |
-
''' Loading the atom and bond decoders'''
|
424 |
-
|
425 |
-
with open("data/decoders/" + dictionary_name + "_" + self.dataset_name + '.pkl', 'rb') as f:
|
426 |
-
|
427 |
-
return pickle.load(f)
|
428 |
-
|
429 |
-
def drug_decoder_load(self, dictionary_name):
|
430 |
-
|
431 |
-
''' Loading the atom and bond decoders'''
|
432 |
-
|
433 |
-
with open("data/decoders/" + dictionary_name +"_" + self.drugs_name +'.pkl', 'rb') as f:
|
434 |
-
|
435 |
-
return pickle.load(f)
|
436 |
-
|
437 |
-
def print_network(self, model, name):
|
438 |
-
|
439 |
-
"""Print out the network information."""
|
440 |
-
|
441 |
-
num_params = 0
|
442 |
-
for p in model.parameters():
|
443 |
-
num_params += p.numel()
|
444 |
-
print(model)
|
445 |
-
print(name)
|
446 |
-
print("The number of parameters: {}".format(num_params))
|
447 |
-
|
448 |
-
|
449 |
-
def restore_model(self, epoch, iteration, model_directory):
|
450 |
-
|
451 |
-
"""Restore the trained generator and discriminator."""
|
452 |
-
|
453 |
-
print('Loading the trained models from epoch / iteration {}-{}...'.format(epoch, iteration))
|
454 |
-
|
455 |
-
G_path = os.path.join(model_directory, '{}-{}-G.ckpt'.format(epoch, iteration))
|
456 |
-
D_path = os.path.join(model_directory, '{}-{}-D.ckpt'.format(epoch, iteration))
|
457 |
-
|
458 |
-
self.G.load_state_dict(torch.load(G_path, map_location=lambda storage, loc: storage))
|
459 |
-
self.D.load_state_dict(torch.load(D_path, map_location=lambda storage, loc: storage))
|
460 |
-
|
461 |
-
|
462 |
-
G2_path = os.path.join(model_directory, '{}-{}-G2.ckpt'.format(epoch, iteration))
|
463 |
-
D2_path = os.path.join(model_directory, '{}-{}-D2.ckpt'.format(epoch, iteration))
|
464 |
-
|
465 |
-
self.G2.load_state_dict(torch.load(G2_path, map_location=lambda storage, loc: storage))
|
466 |
-
self.D2.load_state_dict(torch.load(D2_path, map_location=lambda storage, loc: storage))
|
467 |
-
|
468 |
-
|
469 |
-
def save_model(self, model_directory, idx,i):
|
470 |
-
G_path = os.path.join(model_directory, '{}-{}-G.ckpt'.format(idx+1,i+1))
|
471 |
-
D_path = os.path.join(model_directory, '{}-{}-D.ckpt'.format(idx+1,i+1))
|
472 |
-
torch.save(self.G.state_dict(), G_path)
|
473 |
-
torch.save(self.D.state_dict(), D_path)
|
474 |
-
|
475 |
-
if self.submodel != "NoTarget" and self.submodel != "CrossLoss":
|
476 |
-
G2_path = os.path.join(model_directory, '{}-{}-G2.ckpt'.format(idx+1,i+1))
|
477 |
-
D2_path = os.path.join(model_directory, '{}-{}-D2.ckpt'.format(idx+1,i+1))
|
478 |
-
|
479 |
-
torch.save(self.G2.state_dict(), G2_path)
|
480 |
-
torch.save(self.D2.state_dict(), D2_path)
|
481 |
-
|
482 |
-
def reset_grad(self):
|
483 |
-
|
484 |
-
"""Reset the gradient buffers."""
|
485 |
-
|
486 |
-
self.g_optimizer.zero_grad()
|
487 |
-
self.v_optimizer.zero_grad()
|
488 |
-
self.g2_optimizer.zero_grad()
|
489 |
-
self.v2_optimizer.zero_grad()
|
490 |
-
|
491 |
-
self.d_optimizer.zero_grad()
|
492 |
-
self.d2_optimizer.zero_grad()
|
493 |
-
|
494 |
-
def gradient_penalty(self, y, x):
|
495 |
-
|
496 |
-
"""Compute gradient penalty: (L2_norm(dy/dx) - 1)**2."""
|
497 |
-
|
498 |
-
weight = torch.ones(y.size(),requires_grad=False).to(self.device)
|
499 |
-
dydx = torch.autograd.grad(outputs=y,
|
500 |
-
inputs=x,
|
501 |
-
grad_outputs=weight,
|
502 |
-
retain_graph=True,
|
503 |
-
create_graph=True,
|
504 |
-
only_inputs=True)[0]
|
505 |
-
|
506 |
-
dydx = dydx.view(dydx.size(0), -1)
|
507 |
-
gradient_penalty = ((dydx.norm(2, dim=1) - 1) ** 2).mean()
|
508 |
-
|
509 |
-
return gradient_penalty
|
510 |
-
|
511 |
-
def train(self):
|
512 |
-
|
513 |
-
''' Training Script starts from here'''
|
514 |
-
|
515 |
-
#wandb.config = {'beta2': 0.999}
|
516 |
-
#wandb.init(project="DrugGEN2", entity="atabeyunlu")
|
517 |
-
|
518 |
-
# Defining sampling paths and creating logger
|
519 |
-
|
520 |
-
self.arguments = "{}_glr{}_dlr{}_g2lr{}_d2lr{}_dim{}_depth{}_heads{}_decdepth{}_decheads{}_ncritic{}_batch{}_epoch{}_warmup{}_dataset{}_dropout{}".format(self.submodel,self.g_lr,self.d_lr,self.g2_lr,self.d2_lr,self.dim,self.depth,self.heads,self.dec_depth,self.dec_heads,self.n_critic,self.batch_size,self.epoch,self.warm_up_steps,self.dataset_name,self.dropout)
|
521 |
-
|
522 |
-
self.model_directory= os.path.join(self.model_save_dir,self.arguments)
|
523 |
-
self.sample_directory=os.path.join(self.sample_dir,self.arguments)
|
524 |
-
self.log_path = os.path.join(self.log_dir, "{}.txt".format(self.arguments))
|
525 |
-
if not os.path.exists(self.model_directory):
|
526 |
-
os.makedirs(self.model_directory)
|
527 |
-
if not os.path.exists(self.sample_directory):
|
528 |
-
os.makedirs(self.sample_directory)
|
529 |
-
|
530 |
-
# Learning rate cache for decaying.
|
531 |
-
|
532 |
-
|
533 |
-
# protein data
|
534 |
-
full_smiles = [line for line in open("data/chembl_train.smi", 'r').read().splitlines()]
|
535 |
-
drug_smiles = [line for line in open("data/akt_train.smi", 'r').read().splitlines()]
|
536 |
-
|
537 |
-
drug_mols = [Chem.MolFromSmiles(smi) for smi in drug_smiles]
|
538 |
-
drug_scaf = [MurckoScaffold.GetScaffoldForMol(x) for x in drug_mols]
|
539 |
-
fps_r = [Chem.RDKFingerprint(x) for x in drug_scaf]
|
540 |
-
|
541 |
-
akt1_human_adj = torch.load("data/akt/AKT1_human_adj.pt").reshape(1,-1).to(self.device).float()
|
542 |
-
akt1_human_annot = torch.load("data/akt/AKT1_human_annot.pt").reshape(1,-1).to(self.device).float()
|
543 |
-
|
544 |
-
if self.resume:
|
545 |
-
self.restore_model(self.resume_epoch, self.resume_iter, self.resume_directory)
|
546 |
-
|
547 |
-
# Start training.
|
548 |
-
|
549 |
-
print('Start training...')
|
550 |
-
self.start_time = time.time()
|
551 |
-
for idx in range(self.epoch):
|
552 |
-
|
553 |
-
# =================================================================================== #
|
554 |
-
# 1. Preprocess input data #
|
555 |
-
# =================================================================================== #
|
556 |
-
|
557 |
-
# Load the data
|
558 |
-
|
559 |
-
dataloader_iterator = iter(self.drugs_loader)
|
560 |
-
|
561 |
-
for i, data in enumerate(self.loader):
|
562 |
-
try:
|
563 |
-
drugs = next(dataloader_iterator)
|
564 |
-
except StopIteration:
|
565 |
-
dataloader_iterator = iter(self.drugs_loader)
|
566 |
-
drugs = next(dataloader_iterator)
|
567 |
-
|
568 |
-
# Preprocess both dataset
|
569 |
-
|
570 |
-
bulk_data = load_data(data,
|
571 |
-
drugs,
|
572 |
-
self.batch_size,
|
573 |
-
self.device,
|
574 |
-
self.b_dim,
|
575 |
-
self.m_dim,
|
576 |
-
self.drugs_b_dim,
|
577 |
-
self.drugs_m_dim,
|
578 |
-
self.z_dim,
|
579 |
-
self.vertexes)
|
580 |
-
|
581 |
-
drug_graphs, real_graphs, a_tensor, x_tensor, drugs_a_tensor, drugs_x_tensor, z, z_edge, z_node = bulk_data
|
582 |
-
|
583 |
-
if self.submodel == "CrossLoss":
|
584 |
-
GAN1_input_e = a_tensor
|
585 |
-
GAN1_input_x = x_tensor
|
586 |
-
GAN1_disc_e = drugs_a_tensor
|
587 |
-
GAN1_disc_x = drugs_x_tensor
|
588 |
-
elif self.submodel == "Ligand":
|
589 |
-
GAN1_input_e = a_tensor
|
590 |
-
GAN1_input_x = x_tensor
|
591 |
-
GAN1_disc_e = a_tensor
|
592 |
-
GAN1_disc_x = x_tensor
|
593 |
-
GAN2_input_e = drugs_a_tensor
|
594 |
-
GAN2_input_x = drugs_x_tensor
|
595 |
-
GAN2_disc_e = drugs_a_tensor
|
596 |
-
GAN2_disc_x = drugs_x_tensor
|
597 |
-
elif self.submodel == "Prot":
|
598 |
-
GAN1_input_e = a_tensor
|
599 |
-
GAN1_input_x = x_tensor
|
600 |
-
GAN1_disc_e = a_tensor
|
601 |
-
GAN1_disc_x = x_tensor
|
602 |
-
GAN2_input_e = akt1_human_adj
|
603 |
-
GAN2_input_x = akt1_human_annot
|
604 |
-
GAN2_disc_e = drugs_a_tensor
|
605 |
-
GAN2_disc_x = drugs_x_tensor
|
606 |
-
elif self.submodel == "RL":
|
607 |
-
GAN1_input_e = a_tensor
|
608 |
-
GAN1_input_x = x_tensor
|
609 |
-
GAN1_disc_e = a_tensor
|
610 |
-
GAN1_disc_x = x_tensor
|
611 |
-
GAN2_input_e = drugs_a_tensor
|
612 |
-
GAN2_input_x = drugs_x_tensor
|
613 |
-
GAN2_disc_e = drugs_a_tensor
|
614 |
-
GAN2_disc_x = drugs_x_tensor
|
615 |
-
elif self.submodel == "NoTarget":
|
616 |
-
GAN1_input_e = a_tensor
|
617 |
-
GAN1_input_x = x_tensor
|
618 |
-
GAN1_disc_e = a_tensor
|
619 |
-
GAN1_disc_x = x_tensor
|
620 |
-
|
621 |
-
# =================================================================================== #
|
622 |
-
# 2. Train the discriminator #
|
623 |
-
# =================================================================================== #
|
624 |
-
loss = {}
|
625 |
-
self.reset_grad()
|
626 |
-
|
627 |
-
# Compute discriminator loss.
|
628 |
-
|
629 |
-
node, edge, d_loss = discriminator_loss(self.G,
|
630 |
-
self.D,
|
631 |
-
real_graphs,
|
632 |
-
GAN1_disc_e,
|
633 |
-
GAN1_disc_x,
|
634 |
-
self.batch_size,
|
635 |
-
self.device,
|
636 |
-
self.gradient_penalty,
|
637 |
-
self.lambda_gp,
|
638 |
-
GAN1_input_e,
|
639 |
-
GAN1_input_x)
|
640 |
-
|
641 |
-
d_total = d_loss
|
642 |
-
if self.submodel != "NoTarget" and self.submodel != "CrossLoss":
|
643 |
-
d2_loss = discriminator2_loss(self.G2,
|
644 |
-
self.D2,
|
645 |
-
drug_graphs,
|
646 |
-
edge,
|
647 |
-
node,
|
648 |
-
self.batch_size,
|
649 |
-
self.device,
|
650 |
-
self.gradient_penalty,
|
651 |
-
self.lambda_gp,
|
652 |
-
GAN2_input_e,
|
653 |
-
GAN2_input_x)
|
654 |
-
d_total = d_loss + d2_loss
|
655 |
-
|
656 |
-
loss["d_total"] = d_total.item()
|
657 |
-
d_total.backward()
|
658 |
-
self.d_optimizer.step()
|
659 |
-
if self.submodel != "NoTarget" and self.submodel != "CrossLoss":
|
660 |
-
self.d2_optimizer.step()
|
661 |
-
self.reset_grad()
|
662 |
-
generator_output = generator_loss(self.G,
|
663 |
-
self.D,
|
664 |
-
self.V,
|
665 |
-
GAN1_input_e,
|
666 |
-
GAN1_input_x,
|
667 |
-
self.batch_size,
|
668 |
-
sim_reward,
|
669 |
-
self.dataset.matrices2mol,
|
670 |
-
fps_r,
|
671 |
-
self.submodel,
|
672 |
-
self.dataset_name)
|
673 |
-
|
674 |
-
g_loss, fake_mol, g_edges_hat_sample, g_nodes_hat_sample, node, edge = generator_output
|
675 |
-
|
676 |
-
self.reset_grad()
|
677 |
-
g_total = g_loss
|
678 |
-
if self.submodel != "NoTarget" and self.submodel != "CrossLoss":
|
679 |
-
output = generator2_loss(self.G2,
|
680 |
-
self.D2,
|
681 |
-
self.V2,
|
682 |
-
edge,
|
683 |
-
node,
|
684 |
-
self.batch_size,
|
685 |
-
sim_reward,
|
686 |
-
self.dataset.matrices2mol_drugs,
|
687 |
-
fps_r,
|
688 |
-
GAN2_input_e,
|
689 |
-
GAN2_input_x,
|
690 |
-
self.submodel,
|
691 |
-
self.drugs_name)
|
692 |
-
|
693 |
-
g2_loss, fake_mol_g, dr_g_edges_hat_sample, dr_g_nodes_hat_sample = output
|
694 |
-
|
695 |
-
g_total = g_loss + g2_loss
|
696 |
-
|
697 |
-
loss["g_total"] = g_total.item()
|
698 |
-
g_total.backward()
|
699 |
-
self.g_optimizer.step()
|
700 |
-
if self.submodel != "NoTarget" and self.submodel != "CrossLoss":
|
701 |
-
self.g2_optimizer.step()
|
702 |
-
|
703 |
-
if self.submodel == "RL":
|
704 |
-
self.v_optimizer.step()
|
705 |
-
self.v2_optimizer.step()
|
706 |
-
|
707 |
-
|
708 |
-
if (i+1) % self.log_step == 0:
|
709 |
-
|
710 |
-
logging(self.log_path, self.start_time, fake_mol, full_smiles, i, idx, loss, 1,self.sample_directory)
|
711 |
-
mol_sample(self.sample_directory,"GAN1",fake_mol, g_edges_hat_sample.detach(), g_nodes_hat_sample.detach(), idx, i)
|
712 |
-
if self.submodel != "NoTarget" and self.submodel != "CrossLoss":
|
713 |
-
logging(self.log_path, self.start_time, fake_mol_g, drug_smiles, i, idx, loss, 2,self.sample_directory)
|
714 |
-
mol_sample(self.sample_directory,"GAN2",fake_mol_g, dr_g_edges_hat_sample.detach(), dr_g_nodes_hat_sample.detach(), idx, i)
|
715 |
-
|
716 |
-
|
717 |
-
if (idx+1) % 10 == 0:
|
718 |
-
self.save_model(self.model_directory,idx,i)
|
719 |
-
print("model saved at epoch {} and iteration {}".format(idx,i))
|
720 |
-
|
721 |
-
|
722 |
-
|
723 |
-
def inference(self):
|
724 |
-
|
725 |
-
# Load the trained generator.
|
726 |
-
self.G.to(self.device)
|
727 |
-
self.G2.to(self.device)
|
728 |
-
|
729 |
-
G_path = os.path.join(self.inference_model, '{}-G.ckpt'.format(self.submodel))
|
730 |
-
self.G.load_state_dict(torch.load(G_path, map_location=lambda storage, loc: storage))
|
731 |
-
if self.submodel != "NoTarget" and self.submodel != "CrossLoss":
|
732 |
-
G2_path = os.path.join(self.inference_model, '{}-G2.ckpt'.format(self.submodel))
|
733 |
-
self.G2.load_state_dict(torch.load(G2_path, map_location=lambda storage, loc: storage))
|
734 |
-
|
735 |
-
|
736 |
-
smiles_test = [line for line in open("data/chembl_test.smi", 'r').read().splitlines()]
|
737 |
-
if self.submodel == "NoTarget":
|
738 |
-
smiles_train = [line for line in open("data/chembl_train.smi", 'r').read().splitlines()]
|
739 |
-
else:
|
740 |
-
smiles_train = [line for line in open("data/akt_train.smi", 'r').read().splitlines()]
|
741 |
-
|
742 |
-
if self.submodel == "RL":
|
743 |
-
drug_mols = [Chem.MolFromSmiles(smi) for smi in drug_smiles]
|
744 |
-
drug_scaf = [MurckoScaffold.GetScaffoldForMol(x) for x in drug_mols]
|
745 |
-
fps_r = [Chem.RDKFingerprint(x) for x in drug_scaf]
|
746 |
-
else:
|
747 |
-
fps_r = None
|
748 |
-
akt1_human_adj = torch.load("data/akt/AKT1_human_adj.pt").reshape(1,-1).to(self.device).float()
|
749 |
-
akt1_human_annot = torch.load("data/akt/AKT1_human_annot.pt").reshape(1,-1).to(self.device).float()
|
750 |
-
|
751 |
-
self.G.eval()
|
752 |
-
#self.D.eval()
|
753 |
-
self.G2.eval()
|
754 |
-
#self.D2.eval()
|
755 |
-
|
756 |
-
step = self.inference_iterations
|
757 |
-
|
758 |
-
self.inf_dataset = DruggenDataset(self.mol_data_dir,
|
759 |
-
self.inf_dataset_file,
|
760 |
-
self.inf_raw_file,
|
761 |
-
self.max_atom,
|
762 |
-
self.features) # Dataset for the first GAN. Custom dataset class from PyG parent class.
|
763 |
-
# Can create any molecular graph dataset given smiles string.
|
764 |
-
# Nonisomeric SMILES are suggested but not necessary.
|
765 |
-
# Uses sparse matrix representation for graphs,
|
766 |
-
# For computational and speed efficiency.
|
767 |
-
|
768 |
-
self.inf_loader = DataLoader(self.inf_dataset,
|
769 |
-
shuffle=True,
|
770 |
-
batch_size=self.inf_batch_size,
|
771 |
-
drop_last=True) # PyG dataloader for the first GAN.
|
772 |
-
|
773 |
-
self.inf_drugs = DruggenDataset(self.drug_data_dir,
|
774 |
-
self.inf_drugs_dataset_file,
|
775 |
-
self.inf_drug_raw_file,
|
776 |
-
self.max_atom,
|
777 |
-
self.features) # Dataset for the second GAN. Custom dataset class from PyG parent class.
|
778 |
-
# Can create any molecular graph dataset given smiles string.
|
779 |
-
# Nonisomeric SMILES are suggested but not necessary.
|
780 |
-
# Uses sparse matrix representation for graphs,
|
781 |
-
# For computational and speed efficiency.
|
782 |
-
|
783 |
-
self.inf_drugs_loader = DataLoader(self.inf_drugs,
|
784 |
-
shuffle=True,
|
785 |
-
batch_size=self.inf_batch_size,
|
786 |
-
drop_last=True) # PyG dataloader for the second GAN.
|
787 |
-
start_time = time.time()
|
788 |
-
#metric_calc_mol = []
|
789 |
-
metric_calc_dr = []
|
790 |
-
date = time.time()
|
791 |
-
if not os.path.exists("experiments/inference/{}".format(self.submodel)):
|
792 |
-
os.makedirs("experiments/inference/{}".format(self.submodel))
|
793 |
-
with torch.inference_mode():
|
794 |
-
|
795 |
-
dataloader_iterator = iter(self.inf_drugs_loader)
|
796 |
-
pbar = tqdm(range(self.inference_sample_num))
|
797 |
-
pbar.set_description('Inference mode for {} model started'.format(self.submodel))
|
798 |
-
for i, data in enumerate(self.inf_loader):
|
799 |
-
try:
|
800 |
-
drugs = next(dataloader_iterator)
|
801 |
-
except StopIteration:
|
802 |
-
dataloader_iterator = iter(self.inf_drugs_loader)
|
803 |
-
drugs = next(dataloader_iterator)
|
804 |
-
|
805 |
-
# Preprocess both dataset
|
806 |
-
|
807 |
-
bulk_data = load_data(data,
|
808 |
-
drugs,
|
809 |
-
self.inf_batch_size,
|
810 |
-
self.device,
|
811 |
-
self.b_dim,
|
812 |
-
self.m_dim,
|
813 |
-
self.drugs_b_dim,
|
814 |
-
self.drugs_m_dim,
|
815 |
-
self.z_dim,
|
816 |
-
self.vertexes)
|
817 |
-
|
818 |
-
drug_graphs, real_graphs, a_tensor, x_tensor, drugs_a_tensor, drugs_x_tensor, z, z_edge, z_node = bulk_data
|
819 |
-
|
820 |
-
if self.submodel == "CrossLoss":
|
821 |
-
GAN1_input_e = a_tensor
|
822 |
-
GAN1_input_x = x_tensor
|
823 |
-
GAN1_disc_e = drugs_a_tensor
|
824 |
-
GAN1_disc_x = drugs_x_tensor
|
825 |
-
elif self.submodel == "Ligand":
|
826 |
-
GAN1_input_e = a_tensor
|
827 |
-
GAN1_input_x = x_tensor
|
828 |
-
GAN1_disc_e = a_tensor
|
829 |
-
GAN1_disc_x = x_tensor
|
830 |
-
GAN2_input_e = drugs_a_tensor
|
831 |
-
GAN2_input_x = drugs_x_tensor
|
832 |
-
GAN2_disc_e = drugs_a_tensor
|
833 |
-
GAN2_disc_x = drugs_x_tensor
|
834 |
-
elif self.submodel == "Prot":
|
835 |
-
GAN1_input_e = a_tensor
|
836 |
-
GAN1_input_x = x_tensor
|
837 |
-
GAN1_disc_e = a_tensor
|
838 |
-
GAN1_disc_x = x_tensor
|
839 |
-
GAN2_input_e = akt1_human_adj
|
840 |
-
GAN2_input_x = akt1_human_annot
|
841 |
-
GAN2_disc_e = drugs_a_tensor
|
842 |
-
GAN2_disc_x = drugs_x_tensor
|
843 |
-
elif self.submodel == "RL":
|
844 |
-
GAN1_input_e = a_tensor
|
845 |
-
GAN1_input_x = x_tensor
|
846 |
-
GAN1_disc_e = a_tensor
|
847 |
-
GAN1_disc_x = x_tensor
|
848 |
-
GAN2_input_e = drugs_a_tensor
|
849 |
-
GAN2_input_x = drugs_x_tensor
|
850 |
-
GAN2_disc_e = drugs_a_tensor
|
851 |
-
GAN2_disc_x = drugs_x_tensor
|
852 |
-
elif self.submodel == "NoTarget":
|
853 |
-
GAN1_input_e = a_tensor
|
854 |
-
GAN1_input_x = x_tensor
|
855 |
-
GAN1_disc_e = a_tensor
|
856 |
-
GAN1_disc_x = x_tensor
|
857 |
-
# =================================================================================== #
|
858 |
-
# 2. GAN1 Inference #
|
859 |
-
# =================================================================================== #
|
860 |
-
generator_output = generator_loss(self.G,
|
861 |
-
self.D,
|
862 |
-
self.V,
|
863 |
-
GAN1_input_e,
|
864 |
-
GAN1_input_x,
|
865 |
-
self.inf_batch_size,
|
866 |
-
sim_reward,
|
867 |
-
self.dataset.matrices2mol,
|
868 |
-
fps_r,
|
869 |
-
self.submodel,
|
870 |
-
self.dataset_name)
|
871 |
-
|
872 |
-
_, fake_mol_g, _, _, node, edge = generator_output
|
873 |
-
|
874 |
-
# =================================================================================== #
|
875 |
-
# 3. GAN2 Inference #
|
876 |
-
# =================================================================================== #
|
877 |
-
|
878 |
-
if self.submodel != "NoTarget" and self.submodel != "CrossLoss":
|
879 |
-
output = generator2_loss(self.G2,
|
880 |
-
self.D2,
|
881 |
-
self.V2,
|
882 |
-
edge,
|
883 |
-
node,
|
884 |
-
self.inf_batch_size,
|
885 |
-
sim_reward,
|
886 |
-
self.dataset.matrices2mol_drugs,
|
887 |
-
fps_r,
|
888 |
-
GAN2_input_e,
|
889 |
-
GAN2_input_x,
|
890 |
-
self.submodel,
|
891 |
-
self.drugs_name)
|
892 |
-
|
893 |
-
_, fake_mol_g, edges, nodes = output
|
894 |
-
|
895 |
-
inference_drugs = [Chem.MolToSmiles(line) for line in fake_mol_g if line is not None]
|
896 |
-
inference_drugs = [None if x is None else max(x.split('.'), key=len) for x in inference_drugs]
|
897 |
-
|
898 |
-
with open("experiments/inference/{}/inference_drugs.txt".format(self.submodel), "a") as f:
|
899 |
-
for molecules in inference_drugs:
|
900 |
-
|
901 |
-
f.write(molecules)
|
902 |
-
f.write("\n")
|
903 |
-
metric_calc_dr.append(molecules)
|
904 |
-
|
905 |
-
if len(inference_drugs) > 0:
|
906 |
-
pbar.update(1)
|
907 |
-
|
908 |
-
if len(metric_calc_dr) == self.inference_sample_num:
|
909 |
-
break
|
910 |
-
|
911 |
-
et = time.time() - start_time
|
912 |
-
|
913 |
-
print("Inference mode is lasted for {:.2f} seconds".format(et))
|
914 |
-
|
915 |
-
# print("Metrics calculation started using MOSES.")
|
916 |
-
return {
|
917 |
-
"Runtime (seconds)": round(et, 2),
|
918 |
-
"Validity": f"{fraction_valid(metric_calc_dr)*100:.2f}%",
|
919 |
-
"Uniqueness": f"{fraction_unique(metric_calc_dr)*100:.2f}%",
|
920 |
-
"Novelty (Train)": f"{novelty(metric_calc_dr, smiles_train)*100:.2f}%",
|
921 |
-
"Novelty (Inference)": f"{novelty(metric_calc_dr, smiles_test)*100:.2f}%"
|
922 |
-
}
|
923 |
-
# print("Validity: ", fraction_valid(metric_calc_dr), "\n")
|
924 |
-
# print("Uniqueness: ", fraction_unique(metric_calc_dr), "\n")
|
925 |
-
# print("Validity: ", novelty(metric_calc_dr, drug_smiles), "\n")
|
926 |
-
|
927 |
-
# print("Metrics are calculated.")
|
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training_data.py
DELETED
@@ -1,50 +0,0 @@
|
|
1 |
-
import torch
|
2 |
-
import torch_geometric.utils as geoutils
|
3 |
-
from utils import *
|
4 |
-
|
5 |
-
def load_data(data, drugs, batch_size, device, b_dim, m_dim, drugs_b_dim, drugs_m_dim,z_dim,vertexes):
|
6 |
-
|
7 |
-
z = sample_z(batch_size, z_dim) # (batch,max_len)
|
8 |
-
|
9 |
-
z = torch.from_numpy(z).to(device).float().requires_grad_(True)
|
10 |
-
data = data.to(device)
|
11 |
-
drugs = drugs.to(device)
|
12 |
-
z_e = sample_z_edge(batch_size,vertexes,b_dim) # (batch,max_len,max_len)
|
13 |
-
z_n = sample_z_node(batch_size,vertexes,m_dim) # (batch,max_len)
|
14 |
-
z_edge = torch.from_numpy(z_e).to(device).float().requires_grad_(True) # Edge noise.(batch,max_len,max_len)
|
15 |
-
z_node = torch.from_numpy(z_n).to(device).float().requires_grad_(True) # Node noise.(batch,max_len)
|
16 |
-
a = geoutils.to_dense_adj(edge_index = data.edge_index,batch=data.batch,edge_attr=data.edge_attr, max_num_nodes=int(data.batch.shape[0]/batch_size))
|
17 |
-
x = data.x.view(batch_size,int(data.batch.shape[0]/batch_size),-1)
|
18 |
-
|
19 |
-
a_tensor = label2onehot(a, b_dim, device)
|
20 |
-
#x_tensor = label2onehot(x, m_dim)
|
21 |
-
x_tensor = x
|
22 |
-
|
23 |
-
a_tensor = a_tensor #+ torch.randn([a_tensor.size(0), a_tensor.size(1), a_tensor.size(2),1], device=a_tensor.device) * noise_strength_0
|
24 |
-
x_tensor = x_tensor #+ torch.randn([x_tensor.size(0), x_tensor.size(1),1], device=x_tensor.device) * noise_strength_1
|
25 |
-
|
26 |
-
drugs_a = geoutils.to_dense_adj(edge_index = drugs.edge_index,batch=drugs.batch,edge_attr=drugs.edge_attr, max_num_nodes=int(drugs.batch.shape[0]/batch_size))
|
27 |
-
|
28 |
-
drugs_x = drugs.x.view(batch_size,int(drugs.batch.shape[0]/batch_size),-1)
|
29 |
-
|
30 |
-
drugs_a = drugs_a.to(device).long()
|
31 |
-
drugs_x = drugs_x.to(device)
|
32 |
-
drugs_a_tensor = label2onehot(drugs_a, drugs_b_dim,device).float()
|
33 |
-
drugs_x_tensor = drugs_x
|
34 |
-
|
35 |
-
drugs_a_tensor = drugs_a_tensor #+ torch.randn([drugs_a_tensor.size(0), drugs_a_tensor.size(1), drugs_a_tensor.size(2),1], device=drugs_a_tensor.device) * noise_strength_2
|
36 |
-
drugs_x_tensor = drugs_x_tensor #+ torch.randn([drugs_x_tensor.size(0), drugs_x_tensor.size(1),1], device=drugs_x_tensor.device) * noise_strength_3
|
37 |
-
#prot_n = akt1_human_annot[None,:].to(device).float()
|
38 |
-
#prot_e = akt1_human_adj[None,None,:].view(1,546,546,1).to(device).float()
|
39 |
-
|
40 |
-
|
41 |
-
|
42 |
-
a_tensor_vec = a_tensor.reshape(batch_size,-1)
|
43 |
-
x_tensor_vec = x_tensor.reshape(batch_size,-1)
|
44 |
-
real_graphs = torch.concat((x_tensor_vec,a_tensor_vec),dim=-1)
|
45 |
-
|
46 |
-
a_drug_vec = drugs_a_tensor.reshape(batch_size,-1)
|
47 |
-
x_drug_vec = drugs_x_tensor.reshape(batch_size,-1)
|
48 |
-
drug_graphs = torch.concat((x_drug_vec,a_drug_vec),dim=-1)
|
49 |
-
|
50 |
-
return drug_graphs, real_graphs, a_tensor, x_tensor, drugs_a_tensor, drugs_x_tensor, z, z_edge, z_node
|
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|
utils.py
DELETED
@@ -1,442 +0,0 @@
|
|
1 |
-
from statistics import mean
|
2 |
-
from rdkit import DataStructs
|
3 |
-
from rdkit import Chem
|
4 |
-
from rdkit.Chem import AllChem
|
5 |
-
from rdkit.Chem import Draw
|
6 |
-
import os
|
7 |
-
import numpy as np
|
8 |
-
#import seaborn as sns
|
9 |
-
import matplotlib.pyplot as plt
|
10 |
-
from matplotlib.lines import Line2D
|
11 |
-
from rdkit import RDLogger
|
12 |
-
import torch
|
13 |
-
from rdkit.Chem.Scaffolds import MurckoScaffold
|
14 |
-
import math
|
15 |
-
import time
|
16 |
-
import datetime
|
17 |
-
import re
|
18 |
-
RDLogger.DisableLog('rdApp.*')
|
19 |
-
import warnings
|
20 |
-
from multiprocessing import Pool
|
21 |
-
class Metrics(object):
|
22 |
-
|
23 |
-
@staticmethod
|
24 |
-
def valid(x):
|
25 |
-
return x is not None and Chem.MolToSmiles(x) != ''
|
26 |
-
|
27 |
-
@staticmethod
|
28 |
-
def tanimoto_sim_1v2(data1, data2):
|
29 |
-
min_len = data1.size if data1.size > data2.size else data2
|
30 |
-
sims = []
|
31 |
-
for i in range(min_len):
|
32 |
-
sim = DataStructs.FingerprintSimilarity(data1[i], data2[i])
|
33 |
-
sims.append(sim)
|
34 |
-
mean_sim = mean(sim)
|
35 |
-
return mean_sim
|
36 |
-
|
37 |
-
@staticmethod
|
38 |
-
def mol_length(x):
|
39 |
-
if x is not None:
|
40 |
-
return len([char for char in max(Chem.MolToSmiles(x).split(sep =".")).upper() if char.isalpha()])
|
41 |
-
else:
|
42 |
-
return 0
|
43 |
-
|
44 |
-
@staticmethod
|
45 |
-
def max_component(data, max_len):
|
46 |
-
|
47 |
-
return (np.array(list(map(Metrics.mol_length, data)), dtype=np.float32)/max_len).mean()
|
48 |
-
|
49 |
-
|
50 |
-
def sim_reward(mol_gen, fps_r):
|
51 |
-
|
52 |
-
gen_scaf = []
|
53 |
-
|
54 |
-
for x in mol_gen:
|
55 |
-
if x is not None:
|
56 |
-
try:
|
57 |
-
|
58 |
-
gen_scaf.append(MurckoScaffold.GetScaffoldForMol(x))
|
59 |
-
except:
|
60 |
-
pass
|
61 |
-
|
62 |
-
if len(gen_scaf) == 0:
|
63 |
-
|
64 |
-
rew = 1
|
65 |
-
else:
|
66 |
-
fps = [Chem.RDKFingerprint(x) for x in gen_scaf]
|
67 |
-
|
68 |
-
|
69 |
-
fps = np.array(fps)
|
70 |
-
fps_r = np.array(fps_r)
|
71 |
-
|
72 |
-
rew = average_agg_tanimoto(fps_r,fps)[0]
|
73 |
-
if math.isnan(rew):
|
74 |
-
rew = 1
|
75 |
-
|
76 |
-
return rew ## change this to penalty
|
77 |
-
|
78 |
-
##########################################
|
79 |
-
##########################################
|
80 |
-
##########################################
|
81 |
-
|
82 |
-
def mols2grid_image(mols,path):
|
83 |
-
mols = [e if e is not None else Chem.RWMol() for e in mols]
|
84 |
-
|
85 |
-
for i in range(len(mols)):
|
86 |
-
if Metrics.valid(mols[i]):
|
87 |
-
#if Chem.MolToSmiles(mols[i]) != '':
|
88 |
-
AllChem.Compute2DCoords(mols[i])
|
89 |
-
Draw.MolToFile(mols[i], os.path.join(path,"{}.png".format(i+1)), size=(1200,1200))
|
90 |
-
else:
|
91 |
-
continue
|
92 |
-
|
93 |
-
def save_smiles_matrices(mols,edges_hard, nodes_hard,path,data_source = None):
|
94 |
-
mols = [e if e is not None else Chem.RWMol() for e in mols]
|
95 |
-
|
96 |
-
for i in range(len(mols)):
|
97 |
-
if Metrics.valid(mols[i]):
|
98 |
-
#m0= all_scores_for_print(mols[i], data_source, norm=False)
|
99 |
-
#if Chem.MolToSmiles(mols[i]) != '':
|
100 |
-
save_path = os.path.join(path,"{}.txt".format(i+1))
|
101 |
-
with open(save_path, "a") as f:
|
102 |
-
np.savetxt(f, edges_hard[i].cpu().numpy(), header="edge matrix:\n",fmt='%1.2f')
|
103 |
-
f.write("\n")
|
104 |
-
np.savetxt(f, nodes_hard[i].cpu().numpy(), header="node matrix:\n", footer="\nsmiles:",fmt='%1.2f')
|
105 |
-
f.write("\n")
|
106 |
-
#f.write(m0)
|
107 |
-
f.write("\n")
|
108 |
-
|
109 |
-
|
110 |
-
print(Chem.MolToSmiles(mols[i]), file=open(save_path,"a"))
|
111 |
-
else:
|
112 |
-
continue
|
113 |
-
|
114 |
-
##########################################
|
115 |
-
##########################################
|
116 |
-
##########################################
|
117 |
-
|
118 |
-
def dense_to_sparse_with_attr(adj):
|
119 |
-
###
|
120 |
-
assert adj.dim() >= 2 and adj.dim() <= 3
|
121 |
-
assert adj.size(-1) == adj.size(-2)
|
122 |
-
|
123 |
-
index = adj.nonzero(as_tuple=True)
|
124 |
-
edge_attr = adj[index]
|
125 |
-
|
126 |
-
if len(index) == 3:
|
127 |
-
batch = index[0] * adj.size(-1)
|
128 |
-
index = (batch + index[1], batch + index[2])
|
129 |
-
#index = torch.stack(index, dim=0)
|
130 |
-
return index, edge_attr
|
131 |
-
|
132 |
-
|
133 |
-
def label2onehot(labels, dim, device):
|
134 |
-
|
135 |
-
"""Convert label indices to one-hot vectors."""
|
136 |
-
|
137 |
-
out = torch.zeros(list(labels.size())+[dim]).to(device)
|
138 |
-
out.scatter_(len(out.size())-1,labels.unsqueeze(-1),1.)
|
139 |
-
|
140 |
-
return out.float()
|
141 |
-
|
142 |
-
|
143 |
-
def sample_z_node(batch_size, vertexes, nodes):
|
144 |
-
|
145 |
-
''' Random noise for nodes logits. '''
|
146 |
-
|
147 |
-
return np.random.normal(0,1, size=(batch_size,vertexes, nodes)) # 128, 9, 5
|
148 |
-
|
149 |
-
|
150 |
-
def sample_z_edge(batch_size, vertexes, edges):
|
151 |
-
|
152 |
-
''' Random noise for edges logits. '''
|
153 |
-
|
154 |
-
return np.random.normal(0,1, size=(batch_size, vertexes, vertexes, edges)) # 128, 9, 9, 5
|
155 |
-
|
156 |
-
|
157 |
-
def sample_z( batch_size, z_dim):
|
158 |
-
|
159 |
-
''' Random noise. '''
|
160 |
-
|
161 |
-
return np.random.normal(0,1, size=(batch_size,z_dim)) # 128, 9, 5
|
162 |
-
|
163 |
-
|
164 |
-
def mol_sample(sample_directory, model_name, mol, edges, nodes, idx, i):
|
165 |
-
sample_path = os.path.join(sample_directory,"{}-{}_{}-epoch_iteration".format(model_name,idx+1, i+1))
|
166 |
-
|
167 |
-
if not os.path.exists(sample_path):
|
168 |
-
os.makedirs(sample_path)
|
169 |
-
|
170 |
-
mols2grid_image(mol,sample_path)
|
171 |
-
|
172 |
-
save_smiles_matrices(mol,edges.detach(), nodes.detach(), sample_path)
|
173 |
-
|
174 |
-
if len(os.listdir(sample_path)) == 0:
|
175 |
-
os.rmdir(sample_path)
|
176 |
-
|
177 |
-
print("Valid molecules are saved.")
|
178 |
-
print("Valid matrices and smiles are saved")
|
179 |
-
|
180 |
-
|
181 |
-
def logging(log_path, start_time, mols, train_smiles, i,idx, loss,model_num, save_path, get_maxlen=False):
|
182 |
-
|
183 |
-
gen_smiles = []
|
184 |
-
for line in mols:
|
185 |
-
if line is not None:
|
186 |
-
gen_smiles.append(Chem.MolToSmiles(line))
|
187 |
-
elif line is None:
|
188 |
-
gen_smiles.append(None)
|
189 |
-
|
190 |
-
#gen_smiles_saves = [None if x is None else re.sub('\*', '', x) for x in gen_smiles]
|
191 |
-
#gen_smiles_saves = [None if x is None else re.sub('\.', '', x) for x in gen_smiles_saves]
|
192 |
-
gen_smiles_saves = [None if x is None else max(x.split('.'), key=len) for x in gen_smiles]
|
193 |
-
|
194 |
-
sample_save_dir = os.path.join(save_path, "samples-GAN{}.txt".format(model_num))
|
195 |
-
with open(sample_save_dir, "a") as f:
|
196 |
-
for idxs in range(len(gen_smiles_saves)):
|
197 |
-
if gen_smiles_saves[idxs] is not None:
|
198 |
-
|
199 |
-
f.write(gen_smiles_saves[idxs])
|
200 |
-
f.write("\n")
|
201 |
-
|
202 |
-
k = len(set(gen_smiles_saves) - {None})
|
203 |
-
|
204 |
-
|
205 |
-
et = time.time() - start_time
|
206 |
-
et = str(datetime.timedelta(seconds=et))[:-7]
|
207 |
-
log = "Elapsed [{}], Epoch/Iteration [{}/{}] for GAN{}".format(et, idx, i+1, model_num)
|
208 |
-
|
209 |
-
# Log update
|
210 |
-
#m0 = get_all_metrics(gen = gen_smiles, train = train_smiles, batch_size=batch_size, k = valid_mol_num, device=self.device)
|
211 |
-
valid = fraction_valid(gen_smiles_saves)
|
212 |
-
unique = fraction_unique(gen_smiles_saves, k, check_validity=False)
|
213 |
-
novel = novelty(gen_smiles_saves, train_smiles)
|
214 |
-
|
215 |
-
#qed = [QED(mol) for mol in mols if mol is not None]
|
216 |
-
#sa = [SA(mol) for mol in mols if mol is not None]
|
217 |
-
#logp = [logP(mol) for mol in mols if mol is not None]
|
218 |
-
|
219 |
-
#IntDiv = internal_diversity(gen_smiles)
|
220 |
-
#m0= all_scores_val(fake_mol, mols, full_mols, full_smiles, vert, norm=True) # 'mols' is output of Fake Reward
|
221 |
-
#m1 =all_scores_chem(fake_mol, mols, vert, norm=True)
|
222 |
-
#m0.update(m1)
|
223 |
-
|
224 |
-
if get_maxlen:
|
225 |
-
maxlen = Metrics.max_component(mols, 45)
|
226 |
-
loss.update({"MaxLen": maxlen})
|
227 |
-
|
228 |
-
#m0 = {k: np.array(v).mean() for k, v in m0.items()}
|
229 |
-
#loss.update(m0)
|
230 |
-
loss.update({'Valid': valid})
|
231 |
-
loss.update({'Unique': unique})
|
232 |
-
loss.update({'Novel': novel})
|
233 |
-
#loss.update({'QED': statistics.mean(qed)})
|
234 |
-
#loss.update({'SA': statistics.mean(sa)})
|
235 |
-
#loss.update({'LogP': statistics.mean(logp)})
|
236 |
-
#loss.update({'IntDiv': IntDiv})
|
237 |
-
|
238 |
-
for tag, value in loss.items():
|
239 |
-
|
240 |
-
log += ", {}: {:.4f}".format(tag, value)
|
241 |
-
with open(log_path, "a") as f:
|
242 |
-
f.write(log)
|
243 |
-
f.write("\n")
|
244 |
-
print(log)
|
245 |
-
print("\n")
|
246 |
-
|
247 |
-
|
248 |
-
#def plot_attn(dataset_name, heads,attn_w, model, iter, epoch):
|
249 |
-
#
|
250 |
-
# cols = 4
|
251 |
-
# rows = int(heads/cols)
|
252 |
-
#
|
253 |
-
# fig, axes = plt.subplots( rows,cols, figsize = (30, 14))
|
254 |
-
# axes = axes.flat
|
255 |
-
# attentions_pos = attn_w[0]
|
256 |
-
# attentions_pos = attentions_pos.cpu().detach().numpy()
|
257 |
-
# for i,att in enumerate(attentions_pos):
|
258 |
-
#
|
259 |
-
# #im = axes[i].imshow(att, cmap='gray')
|
260 |
-
# sns.heatmap(att,vmin = 0, vmax = 1,ax = axes[i])
|
261 |
-
# axes[i].set_title(f'head - {i} ')
|
262 |
-
# axes[i].set_ylabel('layers')
|
263 |
-
# pltsavedir = "/home/atabey/attn/second"
|
264 |
-
# plt.savefig(os.path.join(pltsavedir, "attn" + model + "_" + dataset_name + "_" + str(iter) + "_" + str(epoch) + ".png"), dpi= 500,bbox_inches='tight')
|
265 |
-
|
266 |
-
|
267 |
-
def plot_grad_flow(named_parameters, model, iter, epoch):
|
268 |
-
|
269 |
-
# Based on https://discuss.pytorch.org/t/check-gradient-flow-in-network/15063/10
|
270 |
-
'''Plots the gradients flowing through different layers in the net during training.
|
271 |
-
Can be used for checking for possible gradient vanishing / exploding problems.
|
272 |
-
|
273 |
-
Usage: Plug this function in Trainer class after loss.backwards() as
|
274 |
-
"plot_grad_flow(self.model.named_parameters())" to visualize the gradient flow'''
|
275 |
-
ave_grads = []
|
276 |
-
max_grads= []
|
277 |
-
layers = []
|
278 |
-
for n, p in named_parameters:
|
279 |
-
if(p.requires_grad) and ("bias" not in n):
|
280 |
-
print(p.grad,n)
|
281 |
-
layers.append(n)
|
282 |
-
ave_grads.append(p.grad.abs().mean().cpu())
|
283 |
-
max_grads.append(p.grad.abs().max().cpu())
|
284 |
-
plt.bar(np.arange(len(max_grads)), max_grads, alpha=0.1, lw=1, color="c")
|
285 |
-
plt.bar(np.arange(len(max_grads)), ave_grads, alpha=0.1, lw=1, color="b")
|
286 |
-
plt.hlines(0, 0, len(ave_grads)+1, lw=2, color="k" )
|
287 |
-
plt.xticks(range(0,len(ave_grads), 1), layers, rotation="vertical")
|
288 |
-
plt.xlim(left=0, right=len(ave_grads))
|
289 |
-
plt.ylim(bottom = -0.001, top=1) # zoom in on the lower gradient regions
|
290 |
-
plt.xlabel("Layers")
|
291 |
-
plt.ylabel("average gradient")
|
292 |
-
plt.title("Gradient flow")
|
293 |
-
plt.grid(True)
|
294 |
-
plt.legend([Line2D([0], [0], color="c", lw=4),
|
295 |
-
Line2D([0], [0], color="b", lw=4),
|
296 |
-
Line2D([0], [0], color="k", lw=4)], ['max-gradient', 'mean-gradient', 'zero-gradient'])
|
297 |
-
pltsavedir = "/home/atabey/gradients/tryout"
|
298 |
-
plt.savefig(os.path.join(pltsavedir, "weights_" + model + "_" + str(iter) + "_" + str(epoch) + ".png"), dpi= 500,bbox_inches='tight')
|
299 |
-
|
300 |
-
|
301 |
-
def get_mol(smiles_or_mol):
|
302 |
-
'''
|
303 |
-
Loads SMILES/molecule into RDKit's object
|
304 |
-
'''
|
305 |
-
if isinstance(smiles_or_mol, str):
|
306 |
-
if len(smiles_or_mol) == 0:
|
307 |
-
return None
|
308 |
-
mol = Chem.MolFromSmiles(smiles_or_mol)
|
309 |
-
if mol is None:
|
310 |
-
return None
|
311 |
-
try:
|
312 |
-
Chem.SanitizeMol(mol)
|
313 |
-
except ValueError:
|
314 |
-
return None
|
315 |
-
return mol
|
316 |
-
return smiles_or_mol
|
317 |
-
|
318 |
-
|
319 |
-
def mapper(n_jobs):
|
320 |
-
'''
|
321 |
-
Returns function for map call.
|
322 |
-
If n_jobs == 1, will use standard map
|
323 |
-
If n_jobs > 1, will use multiprocessing pool
|
324 |
-
If n_jobs is a pool object, will return its map function
|
325 |
-
'''
|
326 |
-
if n_jobs == 1:
|
327 |
-
def _mapper(*args, **kwargs):
|
328 |
-
return list(map(*args, **kwargs))
|
329 |
-
|
330 |
-
return _mapper
|
331 |
-
if isinstance(n_jobs, int):
|
332 |
-
pool = Pool(n_jobs)
|
333 |
-
|
334 |
-
def _mapper(*args, **kwargs):
|
335 |
-
try:
|
336 |
-
result = pool.map(*args, **kwargs)
|
337 |
-
finally:
|
338 |
-
pool.terminate()
|
339 |
-
return result
|
340 |
-
|
341 |
-
return _mapper
|
342 |
-
return n_jobs.map
|
343 |
-
|
344 |
-
|
345 |
-
def remove_invalid(gen, canonize=True, n_jobs=1):
|
346 |
-
"""
|
347 |
-
Removes invalid molecules from the dataset
|
348 |
-
"""
|
349 |
-
if not canonize:
|
350 |
-
mols = mapper(n_jobs)(get_mol, gen)
|
351 |
-
return [gen_ for gen_, mol in zip(gen, mols) if mol is not None]
|
352 |
-
return [x for x in mapper(n_jobs)(canonic_smiles, gen) if
|
353 |
-
x is not None]
|
354 |
-
|
355 |
-
|
356 |
-
def fraction_valid(gen, n_jobs=1):
|
357 |
-
"""
|
358 |
-
Computes a number of valid molecules
|
359 |
-
Parameters:
|
360 |
-
gen: list of SMILES
|
361 |
-
n_jobs: number of threads for calculation
|
362 |
-
"""
|
363 |
-
gen = mapper(n_jobs)(get_mol, gen)
|
364 |
-
return 1 - gen.count(None) / len(gen)
|
365 |
-
|
366 |
-
|
367 |
-
def canonic_smiles(smiles_or_mol):
|
368 |
-
mol = get_mol(smiles_or_mol)
|
369 |
-
if mol is None:
|
370 |
-
return None
|
371 |
-
return Chem.MolToSmiles(mol)
|
372 |
-
|
373 |
-
|
374 |
-
def fraction_unique(gen, k=None, n_jobs=1, check_validity=True):
|
375 |
-
"""
|
376 |
-
Computes a number of unique molecules
|
377 |
-
Parameters:
|
378 |
-
gen: list of SMILES
|
379 |
-
k: compute unique@k
|
380 |
-
n_jobs: number of threads for calculation
|
381 |
-
check_validity: raises ValueError if invalid molecules are present
|
382 |
-
"""
|
383 |
-
if k is not None:
|
384 |
-
if len(gen) < k:
|
385 |
-
warnings.warn(
|
386 |
-
"Can't compute unique@{}.".format(k) +
|
387 |
-
"gen contains only {} molecules".format(len(gen))
|
388 |
-
)
|
389 |
-
gen = gen[:k]
|
390 |
-
canonic = set(mapper(n_jobs)(canonic_smiles, gen))
|
391 |
-
if None in canonic and check_validity:
|
392 |
-
canonic = [i for i in canonic if i is not None]
|
393 |
-
#raise ValueError("Invalid molecule passed to unique@k")
|
394 |
-
return 0 if len(gen) == 0 else len(canonic) / len(gen)
|
395 |
-
|
396 |
-
|
397 |
-
def novelty(gen, train, n_jobs=1):
|
398 |
-
gen_smiles = mapper(n_jobs)(canonic_smiles, gen)
|
399 |
-
gen_smiles_set = set(gen_smiles) - {None}
|
400 |
-
train_set = set(train)
|
401 |
-
return 0 if len(gen_smiles_set) == 0 else len(gen_smiles_set - train_set) / len(gen_smiles_set)
|
402 |
-
|
403 |
-
|
404 |
-
def average_agg_tanimoto(stock_vecs, gen_vecs,
|
405 |
-
batch_size=5000, agg='max',
|
406 |
-
device='cpu', p=1):
|
407 |
-
"""
|
408 |
-
For each molecule in gen_vecs finds closest molecule in stock_vecs.
|
409 |
-
Returns average tanimoto score for between these molecules
|
410 |
-
|
411 |
-
Parameters:
|
412 |
-
stock_vecs: numpy array <n_vectors x dim>
|
413 |
-
gen_vecs: numpy array <n_vectors' x dim>
|
414 |
-
agg: max or mean
|
415 |
-
p: power for averaging: (mean x^p)^(1/p)
|
416 |
-
"""
|
417 |
-
assert agg in ['max', 'mean'], "Can aggregate only max or mean"
|
418 |
-
agg_tanimoto = np.zeros(len(gen_vecs))
|
419 |
-
total = np.zeros(len(gen_vecs))
|
420 |
-
for j in range(0, stock_vecs.shape[0], batch_size):
|
421 |
-
x_stock = torch.tensor(stock_vecs[j:j + batch_size]).to(device).float()
|
422 |
-
for i in range(0, gen_vecs.shape[0], batch_size):
|
423 |
-
|
424 |
-
y_gen = torch.tensor(gen_vecs[i:i + batch_size]).to(device).float()
|
425 |
-
y_gen = y_gen.transpose(0, 1)
|
426 |
-
tp = torch.mm(x_stock, y_gen)
|
427 |
-
jac = (tp / (x_stock.sum(1, keepdim=True) +
|
428 |
-
y_gen.sum(0, keepdim=True) - tp)).cpu().numpy()
|
429 |
-
jac[np.isnan(jac)] = 1
|
430 |
-
if p != 1:
|
431 |
-
jac = jac**p
|
432 |
-
if agg == 'max':
|
433 |
-
agg_tanimoto[i:i + y_gen.shape[1]] = np.maximum(
|
434 |
-
agg_tanimoto[i:i + y_gen.shape[1]], jac.max(0))
|
435 |
-
elif agg == 'mean':
|
436 |
-
agg_tanimoto[i:i + y_gen.shape[1]] += jac.sum(0)
|
437 |
-
total[i:i + y_gen.shape[1]] += jac.shape[0]
|
438 |
-
if agg == 'mean':
|
439 |
-
agg_tanimoto /= total
|
440 |
-
if p != 1:
|
441 |
-
agg_tanimoto = (agg_tanimoto)**(1/p)
|
442 |
-
return np.mean(agg_tanimoto)
|
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