update

app.py

@@ -1,15 +1,18 @@
 import logging
 import pathlib
+from typing import List
 
 import gradio as gr
+import numpy as np
 import pandas as pd
-from gt4sd.algorithms.generation.torchdrug import (
-    TorchDrugGenerator,
-    TorchDrugGCPN,
-    TorchDrugGraphAF,
+from gt4sd.algorithms.conditional_generation.paccmann_rl import (
+    PaccMannRL,
+    PaccMannRLOmicBasedGenerator,
+    PaccMannRLProteinBasedGenerator,
 )
-
+from gt4sd.algorithms.generation.paccmann_vae import PaccMannVAE, PaccMannVAEGenerator
 from gt4sd.algorithms.registry import ApplicationsRegistry
+
 from utils import draw_grid_generate
 
 logger = logging.getLogger(__name__)
@@ -18,16 +21,48 @@ logger.addHandler(logging.NullHandler())
 TITLE = "MoLeR"
 
 
-def run_inference(algorithm: str, algorithm_version: str, number_of_samples: int):
-
-    if algorithm == "GCPN":
-        config = TorchDrugGCPN(algorithm_version=algorithm_version)
-    elif algorithm == "GraphAF":
-        config = TorchDrugGraphAF(algorithm_version=algorithm_version)
+def run_inference(
+    algorithm_version: str,
+    inference_type: str,
+    protein_target: str,
+    omics_target: str,
+    temperature: float,
+    length: float,
+    number_of_samples: int,
+):
+    if inference_type == "Unbiased":
+        algorithm_class = PaccMannVAEGenerator
+        model_class = PaccMannVAE
+        target = None
+    elif inference_type == "Conditional":
+        if "Protein" in algorithm_version:
+            algorithm_class = PaccMannRLProteinBasedGenerator
+            target = protein_target
+        elif "Omic" in algorithm_version:
+            algorithm_class = PaccMannRLOmicBasedGenerator
+            try:
+                test_target = [float(x) for x in omics_target.split(" ")]
+            except Exception:
+                raise ValueError(
+                    f"Expected 2128 space-separated omics values, got {omics_target}"
+                )
+            if len(test_target) != 2128:
+                raise ValueError(
+                    f"Expected 2128 omics values, got {len(target)}: {target}"
+                )
+            target = omics_target
+        else:
+            raise ValueError(f"Unknown algorithm version {algorithm_version}")
+        model_class = PaccMannRL
     else:
-        raise ValueError(f"Unsupported model {algorithm}.")
+        raise ValueError(f"Unknown inference type {inference_type}")
 
-    model = TorchDrugGenerator(configuration=config)
+    config = algorithm_class(
+        algorithm_version.split("_")[-1],
+        temperature=temperature,
+        generated_length=length,
+    )
+    model = model_class(config, target=target)
     samples = list(model.sample(number_of_samples))
 
     return draw_grid_generate(samples=samples, n_cols=5)
@@ -38,8 +73,10 @@ if __name__ == "__main__":
     # Preparation (retrieve all available algorithms)
     all_algos = ApplicationsRegistry.list_available()
     algos = [
-        x["algorithm_version"]
-        for x in list(filter(lambda x: "TorchDrug" in x["algorithm_name"], all_algos))
+        x["algorithm_application"].split("Based")[0].split("PaccMannRL")[-1]
+        + "_"
+        + x["algorithm_version"]
+        for x in list(filter(lambda x: "PaccMannRL" in x["algorithm_name"], all_algos))
     ]
 
     # Load metadata
@@ -56,11 +93,31 @@ if __name__ == "__main__":
 
     demo = gr.Interface(
         fn=run_inference,
-        title="TorchDrug (GCPN and GraphAF)",
+        title="PaccMannRL",
         inputs=[
-            gr.Dropdown(["GCPN", "GraphAF"], label="Algorithm", value="GCPN"),
-            gr.Dropdown(
-                list(set(algos)), label="Algorithm version", value="zinc250k_v0"
+            gr.Dropdown(algos, label="Algorithm version", value="Protein_v0"),
+            gr.Radio(
+                choices=["Conditional", "Unbiased"],
+                label="Inference type",
+                value="Conditional",
+            ),
+            gr.Textbox(
+                label="Protein target",
+                placeholder="MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT",
+                lines=1,
+            ),
+            gr.Textbox(
+                label="Gene expression target",
+                placeholder=f"{' '.join(map(str, np.round(np.random.rand(2128), 2)))}",
+                lines=1,
+            ),
+            gr.Slider(minimum=0.5, maximum=2, value=1, label="Decoding temperature"),
+            gr.Slider(
+                minimum=5,
+                maximum=400,
+                value=100,
+                label="Maximal sequence length",
+                step=1,
             ),
             gr.Slider(
                 minimum=1, maximum=50, value=10, label="Number of samples", step=1

model_cards/article.md

@@ -1,93 +1,47 @@
 # Model documentation & parameters
 
-**Algorithm**: Which model to use (GCPN or GraphAF).
+**Algorithm Version**: Which model version (either protein-target-driven or gene-expression-profile-driven) to use and which checkpoint to rely on.
 
-**Algorithm Version**: Which model checkpoint to use (trained on different datasets).
+**Inference type**: Whether the model should be conditioned on the target (default) or whether the model is used in an  `Unbiased` manner.
 
-**Number of samples**: How many samples should be generated (between 1 and 50).
-
-
-# Model card -- GCPN
-
-**Model Details**: GCPN is a graph-based molecular generative model that can be optimized with RL for goal-directed graph generation.
-
-**Developers**: Jiaxuan You, Bowen Liu and co-authors from Stanford.
-
-**Distributors**: Code provided by TorchDrug developers, wrapped and distributed by GT4SD Team (2023) from IBM Research.
-
-**Model date**: Published in 2018.
-
-**Model version**: Models trained by GT4SD team on the tasks provided by TorchDrug repo [(see their tutorial)](https://torchdrug.ai/docs/tutorials/generation.html). 
-- **ZINC_250k**: 250,000 drug-like molecules with a maximum atom number of 38, taken from [ZINC](https://zinc.docking.org).
-- **QED**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high QED scores.
-- **pLogP**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high pLogP scores.
-
-**Model type**: A graph-based molecular generative model that can be optimized with RL for goal-directed graph generation.
-
-**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: Default parameters as provided in [(TorchDrug tutorial)](https://torchdrug.ai/docs/tutorials/generation.html).
-
-**Paper or other resource for more information**: [Graph Convolutional Policy Network for
-Goal-Directed Molecular Graph Generation (NeurIPS 2018)](https://proceedings.neurips.cc/paper/2018/file/d60678e8f2ba9c540798ebbde31177e8-Paper.pdf).
-
-**License**: TorchDrug: Apache-2.0 license.
-
-**Where to send questions or comments about the model**: Open an issue on [TorchDrug repository](https://github.com/DeepGraphLearning/torchdrug) or ask original authors.
-
-**Intended Use. Use cases that were envisioned during development**: Chemical research, in particular drug discovery.
-
-**Primary intended uses/users**: Researchers and computational chemists using the model for model comparison or research exploration purposes.
+**Protein target**: An AAS of a protein target used for conditioning. Only use if `Inference type` is `Conditional` and if the `Algorithm version` is a Protein model.
 
-**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
-
-**Factors**: Not applicable.
+**Gene expression target**: A list of 2128 floats, representing the embedding of gene expression profile to be used for conditioning. Only use if `Inference type` is `Conditional` and if the `Algorithm version` is a Omic model.
 
-**Metrics**: Validation loss on decoding correct molecules.
+**Decoding temperature**: The temperature parameter in the SMILES/SELFIES decoder. Higher values lead to more explorative choices, smaller values culminate in mode collapse.
 
-**Datasets**: 250,000 drug-like molecules from [ZINC](https://zinc.docking.org) (with a maximum atom number of 38).
-
-**Ethical Considerations**: Unclear, please consult with original authors in case of questions.
-
-**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.
-
-Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
+**Maximal sequence length**: The maximal number of SMILES tokens in the generated molecule.
 
-## Citation
-
-```bib
-@article{you2018graph,
-  title={Graph convolutional policy network for goal-directed molecular graph generation},
-  author={You, Jiaxuan and Liu, Bowen and Ying, Zhitao and Pande, Vijay and Leskovec, Jure},
-  journal={Advances in neural information processing systems},
-  volume={31},
-  year={2018}
-}
-```
+**Number of samples**: How many samples should be generated (between 1 and 50).
 
 
-# Model card -- GraphAF
+# Model card -- PaccMannRL
 
-**Model Details**: GraphAF is a flow-based autoregressive graph molecular generative model that can be optimized with RL for goal-directed graph generation.
+**Model Details**: PaccMannRL is a language model for conditional molecular design. It consists of a domain-specific encoder (for protein targets or gene expression profiles) and a generic molecular decoder. Both components are finetuned together using RL to convert the context representation into a molecule with high affinity toward the context (i.e., binding affinity to the protein or high inhibitory effect for the cell profile).
 
-**Developers**: Chence Shi, Minkai Xu and co-authors from Peking and Shanghai University and MILA.
+**Developers**: Jannis Born, Matteo Manica and colleagues from IBM Research.
 
-**Distributors**: Code provided by TorchDrug developers, wrapped and distributed by GT4SD Team (2023) from IBM Research.
+**Distributors**: Original authors' code wrapped and distributed by GT4SD Team (2023) from IBM Research.
 
-**Model date**: Published in 2020.
+**Model date**: Published in 2021.
 
-**Model version**: Models trained by GT4SD team on the tasks provided by TorchDrug repo [(see their tutorial)](https://torchdrug.ai/docs/tutorials/generation.html). 
-- **ZINC_250k**: 250,000 drug-like molecules with a maximum atom number of 38, taken from [ZINC](https://zinc.docking.org).
-- **QED**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high QED scores.
-- **pLogP**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high pLogP scores.
+**Model version**: Models trained and distribuetd by the original authors.
+- **Protein_v0**: Molecular decoder pretrained on 1.5M molecules from ChEMBL. Protein encoder pretrained on 404k proteins from UniProt. Encoder and decoder finetuned on 41 SARS-CoV-2-related protein targets with a binding affinity predictor trained on BindingDB.
+- **Omic_v0**: Molecular decoder pretrained on 1.5M molecules from ChEMBL. Gene expression encoder pretrained on 12k gene expression profiles from TCGA. Encoder and decoder finetuned on a few hundred cancer cell profiles from GDSC with a IC50 predictor trained on GDSC.
 
-**Model type**: A flow-based autoregressive graph molecular generative model that can be optimized with RL for goal-directed graph generation.
+**Model type**: A language-based molecular generative model that can be optimized with RL to generate molecules with high affinity toward a context.
 
-**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: Default parameters as provided in [(TorchDrug tutorial)](https://torchdrug.ai/docs/tutorials/generation.html).
+**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: 
+- **Protein**: Parameters as provided on [(GitHub repo)](https://github.com/PaccMann/paccmann_sarscov2).
+- **Omics**: Parameters as provided on [(GitHub repo)](https://github.com/PaccMann/paccmann_rl).
 
-**Paper or other resource for more information**: [GraphAF: a flow-based autoregressive model for molecular graph generation (*ICLR 2020*)](https://openreview.net/pdf?id=S1esMkHYPr).
+**Paper or other resource for more information**: 
+- **Protein**: [PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning (2021; *iScience*)](https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6).
+- **Omics**: [Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2 (2021; *Machine Learning: Science and Technology*)](https://iopscience.iop.org/article/10.1088/2632-2153/abe808/meta).
 
-**License**: TorchDrug: Apache-2.0 license.
+**License**: MIT
 
-**Where to send questions or comments about the model**: Open an issue on [TorchDrug repository](https://github.com/DeepGraphLearning/torchdrug) or ask original authors.
+**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core).
 
 **Intended Use. Use cases that were envisioned during development**: Chemical research, in particular drug discovery.
 
@@ -97,9 +51,9 @@ Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi
 
 **Factors**: Not applicable.
 
-**Metrics**: Validation loss on decoding correct molecules.
+**Metrics**: High reward on generating molecules with high affinity toward context. 
 
-**Datasets**: 250,000 drug-like molecules from [ZINC](https://zinc.docking.org) (with a maximum atom number of 38).
+**Datasets**: ChEMBL, UniProt, GDSC and BindingDB (see above).
 
 **Ethical Considerations**: Unclear, please consult with original authors in case of questions.
 
@@ -109,13 +63,34 @@ Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi
 
 ## Citation
 
+**Omics**:
 ```bib
-@inproceedings{shi2020graphaf,
-  author    = {Chence Shi and Minkai Xu and Zhaocheng Zhu and Weinan Zhang and Ming Zhang and Jian Tang},
-  title     = {GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation},
-  booktitle = {International Conference on Learning Representations, {ICLR} 2020},
-  year      = {2020},
-  url       = {https://openreview.net/forum?id=S1esMkHYPr}
+@article{born2021paccmannrl,
+  title = {PaccMann\textsuperscript{RL}: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning},
+  journal = {iScience},
+  volume = {24},
+  number = {4},
+  pages = {102269},
+  year = {2021},
+  issn = {2589-0042},
+  doi = {https://doi.org/10.1016/j.isci.2021.102269},
+  url = {https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6},
+  author = {Born, Jannis and Manica, Matteo and Oskooei, Ali and Cadow, Joris and Markert, Greta and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a}
 }
 ```
 
+**Proteins**:
+```bib
+@article{born2021datadriven,
+  author = {Born, Jannis and Manica, Matteo and Cadow, Joris and Markert, Greta and Mill, Nil Adell and Filipavicius, Modestas and Janakarajan, Nikita and Cardinale, Antonio and Laino, Teodoro and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a},
+  doi = {10.1088/2632-2153/abe808},
+  issn = {2632-2153},
+  journal = {Machine Learning: Science and Technology},
+  number = {2},
+  pages = {025024},
+  title = {{Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2}},
+  url = {https://iopscience.iop.org/article/10.1088/2632-2153/abe808},
+  volume = {2},
+  year = {2021}
+}
+```

model_cards/description.md

@@ -1,10 +1,9 @@
 <img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
 
+[PaccMann<sup>RL</sup>](https://github.com/PaccMann/paccmann_rl) is a language-based molecular generative model that can be conditioned (primed) on protein targets or gene expression profiles and produces molecules with high affinity toward the context vector. This model has been developed at IBM Research and is distributed by the **GT4SD** (Generative Toolkit for Scientific Discovery) team. For details please see the two publications:
+- [Born et al., (2021), *iScience*](https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6) for the model conditionable on gene expression profiles.
+- [Born et al., (2021), *Machine Learning: Science & Technology*](https://iopscience.iop.org/article/10.1088/2632-2153/abe808/meta) for the model conditionable on protein targets.
 
-[TorchDrug](https://github.com/DeepGraphLearning/torchdrug) is a PyTorch toolbox on graph models for drug discovery.
-We, the developers of **GT4SD** (Generative Toolkit for Scientific Discovery), provide access to two graph-based molecular generative models distributed by TorchDrug:
-- **GCPN**: Graph Convolutional Policy Network ([You et al., (2018), *NeurIPS*](https://proceedings.neurips.cc/paper/2018/hash/d60678e8f2ba9c540798ebbde31177e8-Abstract.html))
-- **GraphAF**: GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation ([Shi et al., (2020), *ICLR*](https://openreview.net/forum?id=S1esMkHYPr))
 
 For **examples** and **documentation** of the model parameters, please see below.
 Moreover, we provide a **model card** ([Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)) at the bottom of this page.

model_cards/examples.csv

@@ -1,4 +1,3 @@
-GCPN,zinc250k_v0,5
-GCPN,qed_v0,10
-GraphAF,plogp_v0,5
-
+Protein_v0,Conditional,MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT,,1.2,100,10
+Protein_v0,Unbiased,,,1.4,250,10
+Omic_v0,Conditional,,0.08 0.9 0.47 0.91 0.7 0.88 0.95 0.37 0.72 0.42 0.63 0.77 0.65 0.83 0.48 0.31 0.36 0.33 0.64 0.33 1.0 0.82 0.49 0.98 0.96 0.86 0.1 0.92 0.13 0.41 0.88 0.79 0.88 0.01 0.3 0.98 0.91 0.83 0.06 0.77 0.56 0.87 0.78 0.27 0.97 0.14 0.71 0.1 0.08 0.63 0.53 0.6 0.66 0.04 0.46 0.6 0.59 0.36 0.65 0.57 0.96 0.42 0.37 0.18 0.71 0.5 0.54 0.22 0.21 0.53 0.66 0.9 0.4 0.95 0.48 0.81 0.47 0.27 0.56 0.77 0.32 0.66 0.01 0.82 0.29 0.81 0.7 0.77 0.65 0.36 0.78 0.31 0.85 0.69 0.12 0.04 0.39 0.11 0.13 0.15 0.35 0.97 0.66 0.35 0.78 0.33 0.48 0.8 0.26 0.05 0.69 0.07 0.92 0.22 0.35 0.13 0.22 0.94 0.73 0.81 0.29 0.3 0.13 0.06 0.9 0.62 0.19 0.69 0.72 0.55 0.34 0.26 0.72 0.95 0.81 0.78 0.5 0.47 0.67 0.49 0.48 0.75 0.52 0.91 0.42 0.62 0.8 0.17 1.0 0.35 0.63 0.02 0.79 0.67 0.99 0.86 0.71 0.15 0.13 0.54 0.19 0.81 0.56 0.98 0.16 0.15 0.69 0.17 0.66 0.74 0.65 0.9 0.73 0.61 0.69 0.19 0.04 0.72 0.41 0.35 0.93 0.91 0.34 0.35 0.92 0.45 0.34 0.52 0.73 0.39 0.54 0.83 0.99 0.68 0.16 0.6 0.48 0.18 0.96 0.7 0.18 0.77 0.6 0.07 0.99 0.97 0.41 0.25 0.98 0.85 0.95 0.59 0.77 0.18 0.22 0.39 0.33 0.46 0.07 0.16 0.81 0.0 0.53 0.49 0.9 0.57 0.03 0.26 0.24 0.57 0.63 0.88 0.57 0.73 0.6 0.71 0.29 0.25 0.94 0.23 0.93 0.07 0.35 0.59 0.66 0.51 0.25 0.51 0.47 0.04 0.85 0.15 0.4 0.51 0.0 0.29 0.29 0.07 0.14 0.77 0.1 0.31 0.95 0.52 0.48 0.24 0.71 0.27 0.93 0.77 0.04 0.92 0.08 0.92 0.68 0.32 0.15 0.77 0.63 0.73 0.14 0.83 0.76 0.96 0.72 0.57 0.92 0.35 0.62 0.21 0.46 0.66 0.89 0.52 0.35 0.71 0.0 0.78 0.51 0.34 0.05 0.57 0.34 0.54 0.57 0.81 0.88 0.61 0.53 0.98 0.26 0.34 0.57 0.94 0.09 0.94 0.15 0.81 0.15 0.83 0.83 0.73 0.33 0.69 0.89 0.46 0.96 0.12 0.82 0.89 0.45 0.26 0.84 0.48 0.51 0.43 0.12 0.74 0.32 0.19 0.8 0.04 0.61 0.63 0.23 0.22 0.7 0.14 0.63 0.35 0.89 0.4 0.1 0.1 0.56 0.98 0.7 0.41 0.78 0.14 0.04 0.97 0.32 0.66 0.54 0.66 0.8 0.86 0.36 0.99 0.01 0.41 0.62 0.81 0.14 0.84 0.49 0.3 0.4 0.13 0.2 0.05 0.29 0.11 0.75 0.87 0.71 0.25 0.43 0.67 0.49 0.2 0.77 0.85 0.32 0.94 0.51 0.95 0.54 0.22 0.7 0.97 0.71 0.24 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requirements.txt

@@ -8,7 +8,7 @@ torch-sparse
 torch-geometric
 torchvision==0.13.1
 torchaudio==0.12.1
-gt4sd>=1.0.0
+gt4sd>=1.0.5
 molgx>=0.22.0a1
 molecule_generation
 nglview