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gyigit commited on 27 days ago

Commit

4d9e86e

1 Parent(s): 14950cf

refactor

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Files changed (50) hide show

app.py +0 -148
data/akt/2x39_X39_BS_adj.csv +0 -0
data/akt/2x39_X39_BS_adj_euc.csv +0 -0
data/akt/2x39_X39_BS_annot.csv +0 -499
data/akt/4gv1_0XZ_BS_adj.csv +0 -0
data/akt/4gv1_0XZ_BS_adj_euc.csv +0 -0
data/akt/4gv1_0XZ_BS_annot.csv +0 -499
data/akt/AKT1_human_annot.pt +0 -0
data/akt/AKT2_human_annot.pt +0 -0
data/akt/AKT3_human_annot.pt +0 -0
data/akt/akt3_0XZ_BS_adj.csv +0 -0
data/akt/akt3_0XZ_BS_adj_euc.csv +0 -0
data/akt/akt3_0XZ_BS_annot.csv +0 -499
data/akt/akt_test.pt +0 -0
data/akt/pre_filter.pt +0 -0
data/akt/pre_transform.pt +0 -0
data/akt1_t5.pth +0 -0
data/akt_inhibitors.smi +0 -0
data/akt_test.smi +0 -320
data/akt_train.smi +0 -0
data/chembl_test.smi +0 -0
data/dataset_download.sh +0 -6
data/{akt → decoders}/.gitkeep +0 -0
data/decoders/atom_akt_test.pkl +0 -0
data/decoders/atom_akt_train.pkl +0 -0
data/decoders/atom_chembl45_test.pkl +0 -0
data/decoders/atom_chembl45_train.pkl +0 -0
data/decoders/atom_drugs_train.pkl +0 -0
data/decoders/bond_akt_test.pkl +0 -0
data/decoders/bond_akt_train.pkl +0 -0
data/decoders/bond_chembl45_test.pkl +0 -0
data/decoders/bond_chembl45_train.pkl +0 -0
data/decoders/bond_drugs_train.pkl +0 -0
data/{decoders/__init__.txt → encoders/.gitkeep} +0 -0
data/encoders/__init__.txt +0 -0
data/encoders/atom_akt_test.pkl +0 -0
data/encoders/atom_akt_train.pkl +0 -0
data/encoders/atom_chembl45_test.pkl +0 -0
data/encoders/atom_chembl45_train.pkl +0 -0
data/encoders/atom_drugs_train.pkl +0 -0
data/encoders/bond_akt_test.pkl +0 -0
data/encoders/bond_akt_train.pkl +0 -0
data/encoders/bond_chembl45_test.pkl +0 -0
data/encoders/bond_chembl45_train.pkl +0 -0
data/encoders/bond_drugs_train.pkl +0 -0
data/filtered_akt_inhibitors.smi +0 -1600
data/papyrus_rnn_S.csv +0 -0
experiments/models/DrugGEN/DrugGEN-G.ckpt +0 -0
gradio_app.py +0 -171
inference.py +0 -288

app.py DELETED Viewed

@@ -1,148 +0,0 @@
-import streamlit as st
-import streamlit_ext as ste
-from inference import Inference
-import random
-from rdkit.Chem import Draw
-from rdkit import Chem
-from rdkit.Chem.Draw import IPythonConsole
-import io
-from PIL import Image
-class DrugGENConfig:
-    submodel='DrugGEN'
-    act='relu'
-    max_atom=45
-    dim=32
-    depth=1
-    heads=8
-    mlp_ratio=3
-    dropout=0.
-    features=False
-    inference_sample_num=1000
-    inf_batch_size=1
-    protein_data_dir='data/akt'
-    drug_index='data/drug_smiles.index'
-    drug_data_dir='data/akt'
-    mol_data_dir='data'
-    log_dir='experiments/logs'
-    model_save_dir='experiments/models'
-    sample_dir='experiments/samples'
-    result_dir="experiments/tboard_output"
-    inf_dataset_file="chembl45_test.pt"
-    inf_drug_dataset_file='akt_test.pt'
-    inf_raw_file='data/chembl_test.smi'
-    inf_drug_raw_file="data/akt_test.smi"
-    inference_model="experiments/models/DrugGEN"
-    log_sample_step=1000
-    set_seed=False
-    seed=1
-class NoTargetConfig(DrugGENConfig):
-    submodel="NoTarget"
-    dim=128
-    inference_model="experiments/models/NoTarget"
-model_configs = {
-    "DrugGEN": DrugGENConfig(),
-    "NoTarget": NoTargetConfig()
-}
-with st.sidebar:
-    st.title("DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
-    st.write("[![arXiv](https://img.shields.io/badge/arXiv-2302.07868-b31b1b.svg)](https://arxiv.org/abs/2302.07868) [![github-repository](https://img.shields.io/badge/GitHub-black?logo=github)](https://github.com/HUBioDataLab/DrugGEN)")
-    with st.expander("Expand to display information about models"):
-        st.write("""
-### Model Variations
-- **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.
-- **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.
-- **DrugGEN-NoTarget**: composed of one GAN, focuses on learning the chemical properties from the ChEMBL training dataset, no target-specific generation.
-        """)
-    with st.form("model_selection_from"):
-        model_name = st.radio(
-            'Select a model to make inference (DrugGEN-Prot and DrugGEN-CrossLoss models design molecules to target the AKT1 protein)',
-            ('DrugGEN-Prot', 'DrugGEN-CrossLoss', 'DrugGEN-NoTarget')
-        )
-        model_name = model_name.replace("DrugGEN-", "")
-        molecule_num_input = st.number_input('Number of molecules to generate', min_value=1, max_value=100_000, value=1000, step=1)
-        seed_input = st.number_input("RNG seed value (can be used for reproducibility):", min_value=0, value=42, step=1)
-        submitted = st.form_submit_button("Start Computing")
-if submitted:
-# if submitted or ("submitted" in st.session_state):
-    # st.session_state["submitted"] = True
-    config = model_configs[model_name]
-    config.inference_sample_num = molecule_num_input
-    config.seed = seed_input
-    with st.spinner(f'Creating the trainer class instance for {model_name}...'):
-        trainer = Trainer(config)
-    with st.spinner(f'Running inference function of {model_name} (this may take a while) ...'):
-        results = trainer.inference()
-    st.success(f"Inference of {model_name} took {results['runtime']:.2f} seconds.")
-    with st.expander("Expand to see the generation performance scores"):
-        st.write("### Generation performance scores (novelty is calculated in comparison to the training dataset)")
-        st.success(f"Validity: {results['fraction_valid']}")
-        st.success(f"Uniqueness: {results['uniqueness']}")
-        st.success(f"Novelty: {results['novelty']}")
-    with open(f'experiments/inference/{model_name}/inference_drugs.txt') as f:
-        inference_drugs = f.read()
-    # st.download_button(label="Click to download generated molecules", data=inference_drugs, file_name=f'DrugGEN-{model_name}_denovo_mols.smi', mime="text/plain")
-    ste.download_button(label="Click to download generated molecules", data=inference_drugs, file_name=f'DrugGEN-{model_name}_denovo_mols.smi', mime="text/plain")
-    st.write("Structures of randomly selected 12 de novo molecules from the inference set:")
-    # from rdkit.Chem import Draw
-# img = Draw.MolsToGridImage(mol_list, molsPerRow=5, subImgSize=(250, 250), maxMols=num_mols,
-                           # legends=None, useSVG=True)
-    generated_molecule_list = inference_drugs.split("\n")
-    selected_molecules = random.choices(generated_molecule_list,k=12)
-    selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules]
-    # IPythonConsole.UninstallIPythonRenderer()
-    drawOptions = Draw.rdMolDraw2D.MolDrawOptions()
-    drawOptions.prepareMolsBeforeDrawing = False
-    drawOptions.bondLineWidth = 1.
-    molecule_image = Draw.MolsToGridImage(
-        selected_molecules,
-        molsPerRow=3,
-        subImgSize=(250, 250),
-        maxMols=len(selected_molecules),
-        # legends=None,
-        returnPNG=False,
-        # drawOptions=drawOptions,
-        highlightAtomLists=None,
-        highlightBondLists=None,
-    )
-    print(type(molecule_image))
-    # print(type(molecule_image._data_and_metadata()))
-    molecule_image.save("result_grid.png")
-    # png_data = io.BytesIO()
-    # molecule_image.save(png_data, format='PNG')
-    # png_data.seek(0)
-    # Step 2: Read the PNG image data as a PIL image
-    # pil_image = Image.open(png_data)
-    # st.image(pil_image)
-    st.image(molecule_image)
-else:
-    st.warning("Please select a model to make inference")

data/akt/2x39_X39_BS_adj.csv DELETED Viewed

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data/akt/2x39_X39_BS_adj_euc.csv DELETED Viewed

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data/akt/2x39_X39_BS_annot.csv DELETED Viewed

@@ -1,499 +0,0 @@
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-#!/bin/sh
-pip install gdown
-gdown --fuzzy -q "https://drive.google.com/file/d/1kDpTm36X3ugpr6Ooo4Fg_dkNRZhQ5EMC/view?usp=share_link"
-gdown --fuzzy -q "https://drive.google.com/file/d/13h465yaIbrAp5tcGbIwhxriorejr6Fsz/view?usp=share_link"

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-Cc1c(N)ncnc1N1CCC(c2nc(-c3ccc(F)c(C(F)(F)F)c3)cn2CCN2CCCC2)CC1
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-COc1cccc2c1-c1nc(-c3ccc(C4(N)CC(O)(C5CC5)C4)cc3)c(-c3ccccc3)n1CO2
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-Cc1ccnc2c1nc(-c1cccnc1N)n2-c1ccc(CN)cc1.Cl
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-Cc1n[nH]c2ccc(-c3cncc(OCC(N)Cc4ccc(C(F)(F)F)cc4)c3)cc12
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-Cn1cc(CC(N)COc2cncc(-c3ccc4cnccc4c3)c2)c2ccccc21
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-COc1ccc(S(=O)(=O)NCc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)cc1
-CC(C)NC(c1ccc(Cl)cc1)c1ccc(-c2ncnc3[nH]cnc23)cc1
-Cc1cccc2c1CCN2C(=O)Cc1nc(N2CCOCC2)cc(=O)n1C
-Cc1n[nH]c2ccc(-c3cc(OCC(N)Cc4ccccc4)cnc3-c3ccc[nH]3)cc12
-O=C1Nc2ccccc2C1=Cc1c[nH]nc1-c1ccccc1[N+](=O)[O-]
-O=C(Nc1ccc(-c2nnc3n2-c2cccnc2Nc2ccccc2-3)cc1)c1cnc2ccccc2n1
-CC(n1cnnc1-c1nc(NC(=O)c2cc(-n3cnc(C4CC4)c3)c(N3CC(N4CCOCC4)C3)cn2)cs1)C(F)(F)F
-NC(=O)c1cc(Cl)c2nc(-c3ccc(C4(N)CCC4)cc3)c(-c3ccccc3)n2c1
-CCOc1cccc2c1-c1nc(-c3ccc(C4(N)CC(O)(C5CC5)C4)cc3)c(-c3ccccc3)n1CO2
-CC1(O)CC(N)(c2ccc(-c3nc4n(c3-c3ccccc3)COc3ncccc3-4)cc2)C1
-Clc1cnc2nc(-c3ccc(CN4CC(c5nnc(-c6ccccn6)[nH]5)C4)cc3)c(-c3ccccc3)cn12
-O=C(NC(c1ccc(Cl)c(Cl)c1)C1CCNC1)c1ccc2cnccc2c1
-CC(C)CCCC1(C)CCc2cc(O)cc(F)c2O1
-COCCOc1ccn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc2n1
-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
-CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCCCN)c21
-COc1cc(Br)cn2c(-c3ccccc3)c(-c3ccc(C4(N)CCC4)cc3)nc12
-NCCC(NC(=O)c1cc(Br)c(-c2ccnc3[nH]ccc23)s1)c1ccccc1
-NC(Cc1ccc(Br)cc1)C(=O)N1CCN(c2ncnc3ccccc23)CC1
-O=C(NC(c1ccc(Cl)c(Cl)c1)C1CCNCC1)c1ccc2cnccc2c1
-[C-]#[N+]c1ccc(C(=O)Nc2ccc(-c3nnc4n3-c3cccnc3Nc3ccccc3-4)cc2)cc1
-CN(C)CCn1cc(-c2ccc(F)c(C(F)(F)F)c2)nc1C1CCN(c2ncnc(N)c2C2CC2)CC1
-Cc1ccc(CC(N)C(=O)N2CCN(c3ncnc4ccccc34)CC2)cc1

data/papyrus_rnn_S.csv DELETED Viewed

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experiments/models/DrugGEN/DrugGEN-G.ckpt DELETED Viewed

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gradio_app.py DELETED Viewed

@@ -1,171 +0,0 @@
-import gradio as gr
-from inference import Inference
-import PIL
-from PIL import Image
-import pandas as pd
-import random
-from rdkit import Chem
-from rdkit.Chem import Draw
-from rdkit.Chem.Draw import IPythonConsole
-import shutil
-import os
-class DrugGENConfig:
-    submodel='DrugGEN'
-    inference_model="experiments/models/DrugGEN/"
-    sample_num=1000
-    inf_dataset_file="chembl45_test.pt"
-    inf_raw_file='data/chembl_test.smi'
-    inf_batch_size=1
-    mol_data_dir='data'
-    features=False
-    act='relu'
-    max_atom=45
-    dim=128
-    depth=1
-    heads=8
-    mlp_ratio=3
-    dropout=0.
-    log_sample_step=100
-    set_seed=True
-    seed=10
-    correct=True
-class NoTargetConfig(DrugGENConfig):
-    submodel="NoTarget"
-    dim=128
-    inference_model="experiments/models/NoTarget/"
-model_configs = {
-    "DrugGEN": DrugGENConfig(),
-    "NoTarget": NoTargetConfig(),
-}
-def function(model_name: str, num_molecules: int, seed_num: int) -> tuple[PIL.Image, pd.DataFrame, str]:
-    '''
-    Returns:
-    image, score_df, file path
-    '''
-    if model_name == "DrugGEN-NoTarget":
-        model_name = "NoTarget"
-    config = model_configs[model_name]
-    config.sample_num = num_molecules
-    if config.sample_num > 250:
-        raise gr.Error("You have requested to generate more than the allowed limit of 250 molecules. Please reduce your request to 250 or fewer.")
-    if seed_num is None or seed_num.strip() == "":
-        config.seed = random.randint(0, 10000)
-    else:
-        try:
-            config.seed = int(seed_num)
-        except ValueError:
-            raise gr.Error("The seed must be an integer value!")
-    inferer = Inference(config)
-    scores = inferer.inference() # create scores_df out of this
-    score_df = pd.DataFrame(scores, index=[0])
-    output_file_path = f'experiments/inference/{model_name}/inference_drugs.txt'
-    new_path = f'{model_name}_denovo_mols.smi'
-    os.rename(output_file_path, new_path)
-    with open(new_path) as f:
-        inference_drugs = f.read()
-    generated_molecule_list = inference_drugs.split("\n")[:-1]
-    rng = random.Random(config.seed)
-    if num_molecules > 12:
-        selected_molecules = rng.choices(generated_molecule_list, k=12)
-    else:
-        selected_molecules = generated_molecule_list
-    selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules if Chem.MolFromSmiles(mol) is not None]
-    drawOptions = Draw.rdMolDraw2D.MolDrawOptions()
-    drawOptions.prepareMolsBeforeDrawing = False
-    drawOptions.bondLineWidth = 0.5
-    molecule_image = Draw.MolsToGridImage(
-        selected_molecules,
-        molsPerRow=3,
-        subImgSize=(400, 400),
-        maxMols=len(selected_molecules),
-        # legends=None,
-        returnPNG=False,
-        drawOptions=drawOptions,
-        highlightAtomLists=None,
-        highlightBondLists=None,
-    )
-    return molecule_image, score_df, new_path
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
-            with gr.Row():
-                gr.Markdown("[![arXiv](https://img.shields.io/badge/arXiv-2302.07868-b31b1b.svg)](https://arxiv.org/abs/2302.07868)")
-                gr.Markdown("[![github-repository](https://img.shields.io/badge/GitHub-black?logo=github)](https://github.com/HUBioDataLab/DrugGEN)")
-            with gr.Accordion("Expand to display information about models", open=False):
-                gr.Markdown("""
-### Model Variations
-- **DrugGEN** is the default model. The input of the generator is the real molecules (ChEMBL) dataset (to ease the learning process) and the discriminator compares the generated molecules with the real inhibitors of the given target protein.
-- **DrugGEN-NoTarget** is the non-target-specific version of DrugGEN. This model only focuses on learning the chemical properties from the ChEMBL training dataset.
-        """)
-            model_name = gr.Radio(
-                choices=("DrugGEN", "DrugGEN-NoTarget"),
-                value="DrugGEN",
-                label="Select a model to make inference",
-                info=str("DrugGEN model designs small molecules to target the human AKT1 protein (UniProt id: P31749)." + '\n'
-                          + "DrugGEN-NoTarget model designs random drug-like small molecules.")
-            )
-            num_molecules = gr.Number(
-                label="Number of molecules to generate",
-                precision=0, # integer input
-                minimum=1,
-                value=100,
-                maximum=999999,
-                info="This space runs on a CPU, which may result in slower performance. Generating 200 molecules takes approximately 6 minutes. Therefore, We set a 250-molecule cap. On a GPU, the model can generate 10,000 molecules in the same amount of time. Please check our GitHub repo for running our models on GPU."
-            )
-            seed_num = gr.Textbox(
-                label="RNG seed value",
-                value=None,
-                info="This is optional, it can be used for reproducibility."
-            )
-            submit_button = gr.Button(
-                value="Start Generating"
-            )
-        with gr.Column(scale=2):
-            scores_df = gr.Dataframe(
-                label="Scores",
-                headers=["Runtime (seconds)", "Validity", "Uniqueness", "Novelty (Train)", "Novelty (Inference)"]
-            )
-            file_download = gr.File(
-                label="Click to download generated molecules",
-            )
-            image_output = gr.Image(
-                label="Structures of randomly selected de novo molecules from the inference set:"
-            )
-    submit_button.click(function, inputs=[model_name, num_molecules, seed_num], outputs=[image_output, scores_df, file_download], api_name="inference")
-#demo.queue(concurrency_count=1)
-demo.queue()
-demo.launch()

inference.py DELETED Viewed

@@ -1,288 +0,0 @@
-import os
-import time
-import pickle
-import random
-from tqdm import tqdm
-import argparse
-import pandas as pd
-import torch
-from torch_geometric.loader import DataLoader
-import torch.utils.data
-from rdkit import RDLogger
-torch.set_num_threads(5)
-RDLogger.DisableLog('rdApp.*')
-from rdkit.Chem import QED
-from utils import *
-from models import Generator
-from new_dataloader import DruggenDataset
-from loss import generator_loss
-from training_data import load_molecules
-from smiles_cor import smi_correct
-class Inference(object):
-    """Inference class for DrugGEN."""
-    def __init__(self, config):
-        if config.set_seed:
-            np.random.seed(config.seed)
-            random.seed(config.seed)
-            torch.manual_seed(config.seed)
-            torch.cuda.manual_seed_all(config.seed)
-            torch.backends.cudnn.deterministic = True
-            torch.backends.cudnn.benchmark = False
-            os.environ["PYTHONHASHSEED"] = str(config.seed)
-            print(f'Using seed {config.seed}')
-        self.device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
-        # Initialize configurations
-        self.submodel = config.submodel
-        self.inference_model = config.inference_model
-        self.sample_num = config.sample_num
-        self.correct = config.correct
-        # Data loader.
-        self.inf_raw_file = config.inf_raw_file  # SMILES containing text file for first dataset.
-                                         # Write the full path to file.
-        self.inf_dataset_file = config.inf_dataset_file    # Dataset file name for the first GAN.
-                                                   # Contains large number of molecules.
-        self.inf_batch_size = config.inf_batch_size
-        self.mol_data_dir = config.mol_data_dir  # Directory where the dataset files are stored.
-        self.dataset_name = self.inf_dataset_file.split(".")[0]
-        self.max_atom = config.max_atom  # Model is based on one-shot generation.
-                                         # Max atom number for molecules must be specified.
-        self.features = config.features  # Small model uses atom types as node features. (Boolean, False uses atom types only.)
-                                         # Additional node features can be added. Please check new_dataloarder.py Line 102.
-        self.inf_dataset = DruggenDataset(self.mol_data_dir,
-                                      self.inf_dataset_file,
-                                      self.inf_raw_file,
-                                      self.max_atom,
-                                      self.features) # Dataset for the first GAN. Custom dataset class from PyG parent class.
-                                                     # Can create any molecular graph dataset given smiles string.
-                                                     # Nonisomeric SMILES are suggested but not necessary.
-                                                     # Uses sparse matrix representation for graphs,
-                                                     # For computational and speed efficiency.
-        self.inf_loader = DataLoader(self.inf_dataset,
-                                 shuffle=True,
-                                 batch_size=self.inf_batch_size,
-                                 drop_last=True)  # PyG dataloader for the first GAN.
-        # Atom and bond type dimensions for the construction of the model.
-        self.atom_decoders = self.decoder_load("atom")  # Atom type decoders for first GAN.
-                                                        # eg. 0:0, 1:6 (C), 2:7 (N), 3:8 (O), 4:9 (F)
-        self.bond_decoders = self.decoder_load("bond")  # Bond type decoders for first GAN.
-                                                        # eg. 0: (no-bond), 1: (single), 2: (double), 3: (triple), 4: (aromatic)
-        self.m_dim = len(self.atom_decoders) if not self.features else int(self.inf_loader.dataset[0].x.shape[1]) # Atom type dimension.
-        self.b_dim = len(self.bond_decoders) # Bond type dimension.
-        self.vertexes = int(self.inf_loader.dataset[0].x.shape[0]) # Number of nodes in the graph.
-        # Transformer and Convolution configurations.
-        self.act = config.act
-        self.dim = config.dim
-        self.depth = config.depth
-        self.heads = config.heads
-        self.mlp_ratio = config.mlp_ratio
-        self.dropout = config.dropout
-        self.build_model()
-    def build_model(self):
-        """Create generators and discriminators."""
-        self.G = Generator(self.act,
-                           self.vertexes,
-                           self.b_dim,
-                           self.m_dim,
-                           self.dropout,
-                           dim=self.dim,
-                           depth=self.depth,
-                           heads=self.heads,
-                           mlp_ratio=self.mlp_ratio)
-        self.print_network(self.G, 'G')
-        self.G.to(self.device)
-    def decoder_load(self, dictionary_name):
-        ''' Loading the atom and bond decoders'''
-        with open("data/decoders/" + dictionary_name + "_" + self.dataset_name + '.pkl', 'rb') as f:
-            return pickle.load(f)
-    def print_network(self, model, name):
-        """Print out the network information."""
-        num_params = 0
-        for p in model.parameters():
-            num_params += p.numel()
-        print(model)
-        print(name)
-        print("The number of parameters: {}".format(num_params))
-    def restore_model(self, submodel, model_directory):
-        """Restore the trained generator and discriminator."""
-        print('Loading the model...')
-        G_path = os.path.join(model_directory, '{}-G.ckpt'.format(submodel))
-        self.G.load_state_dict(torch.load(G_path, map_location=lambda storage, loc: storage))
-    def inference(self):
-        # Load the trained generator.
-        self.restore_model(self.submodel, self.inference_model)
-        # smiles data for metrics calculation.
-        chembl_smiles = [line for line in open("data/chembl_train.smi", 'r').read().splitlines()]
-        chembl_test = [line for line in open("data/chembl_test.smi", 'r').read().splitlines()]
-        drug_smiles = [line for line in open("data/akt_inhibitors.smi", 'r').read().splitlines()]
-        drug_mols = [Chem.MolFromSmiles(smi) for smi in drug_smiles]
-        drug_vecs = [AllChem.GetMorganFingerprintAsBitVect(x, 2, nBits=1024) for x in drug_mols if x is not None]
-        # Make directories if not exist.
-        if not os.path.exists("experiments/inference/{}".format(self.submodel)):
-            os.makedirs("experiments/inference/{}".format(self.submodel))
-        if self.correct:
-            correct = smi_correct(self.submodel, "DrugGEN_/experiments/inference/{}".format(self.submodel))
-        search_res = pd.DataFrame(columns=["submodel", "validity",
-                                           "uniqueness", "novelty",
-                                           "novelty_test", "AKT_novelty",
-                                           "max_len", "mean_atom_type",
-                                           "snn_chembl", "snn_akt", "IntDiv", "qed"])
-        self.G.eval()
-        start_time = time.time()
-        metric_calc_dr = []
-        uniqueness_calc = []
-        real_smiles_snn = []
-        nodes_sample = torch.Tensor(size=[1,45,1]).to(self.device)
-        f = open("experiments/inference/{}/inference_drugs.txt".format(self.submodel), "w")
-        f.write("SMILES")
-        f.write("\n")
-        val_counter = 0
-        none_counter = 0
-        # Inference mode
-        with torch.inference_mode():
-            pbar = tqdm(range(self.sample_num))
-            pbar.set_description('Inference mode for {} model started'.format(self.submodel))
-            for i, data in enumerate(self.inf_loader):
-                val_counter += 1
-                # Preprocess dataset
-                _, a_tensor, x_tensor = load_molecules(
-                    data=data,
-                    batch_size=self.inf_batch_size,
-                    device=self.device,
-                    b_dim=self.b_dim,
-                    m_dim=self.m_dim,
-                )
-                _, _, node_sample, edge_sample = self.G(a_tensor, x_tensor)
-                g_edges_hat_sample = torch.max(edge_sample, -1)[1]
-                g_nodes_hat_sample = torch.max(node_sample, -1)[1]
-                fake_mol_g = [self.inf_dataset.matrices2mol_drugs(n_.data.cpu().numpy(), e_.data.cpu().numpy(), strict=False, file_name=self.dataset_name)
-                        for e_, n_ in zip(g_edges_hat_sample, g_nodes_hat_sample)]
-                a_tensor_sample = torch.max(a_tensor, -1)[1]
-                x_tensor_sample = torch.max(x_tensor, -1)[1]
-                real_mols = [self.inf_dataset.matrices2mol_drugs(n_.data.cpu().numpy(), e_.data.cpu().numpy(), strict=True, file_name=self.dataset_name)
-                        for e_, n_ in zip(a_tensor_sample, x_tensor_sample)]
-                inference_drugs = [None if line is None else Chem.MolToSmiles(line) for line in fake_mol_g]
-                inference_drugs = [None if x is None else max(x.split('.'), key=len) for x in inference_drugs]
-                for molecules in inference_drugs:
-                            if molecules is None:
-                                none_counter += 1
-                for molecules in inference_drugs:
-                    if molecules is not None:
-                        molecules = molecules.replace("*", "C")
-                        f.write(molecules)
-                        f.write("\n")
-                        uniqueness_calc.append(molecules)
-                        nodes_sample = torch.cat((nodes_sample, g_nodes_hat_sample.view(1,45,1)), 0)
-                        pbar.update(1)
-                    metric_calc_dr.append(molecules)
-                real_smiles_snn.append(real_mols[0])
-                generation_number = len([x for x in metric_calc_dr if x is not None])
-                if generation_number == self.sample_num or none_counter == self.sample_num:
-                    break
-        f.close()
-        print("Inference completed, starting metrics calculation.")
-        if self.correct:
-            corrected = correct.correct("experiments/inference/{}/inference_drugs.txt".format(self.submodel))
-            gen_smi = corrected["SMILES"].tolist()
-        else:
-            gen_smi = pd.read_csv("experiments/inference/{}/inference_drugs.txt".format(self.submodel))["SMILES"].tolist()
-        et = time.time() - start_time
-        with open("experiments/inference/{}/inference_drugs.txt".format(self.submodel), "w") as f:
-            for i in gen_smi:
-                f.write(i)
-                f.write("\n")
-        if self.correct:
-            val = round(len(gen_smi)/self.sample_num,3)
-        else:
-            val = round(fraction_valid(gen_smi),3)
-        return{
-            "Runtime (seconds)": f"{et:.2f}",
-            "Validity": str(val),
-            "Uniqueness": f"{fraction_unique(uniqueness_calc):.2f}",
-            "Novelty (Train)": f"{novelty(metric_calc_dr, chembl_smiles):.2f}",
-            "Novelty (Inference)": f"{novelty(metric_calc_dr, chembl_test):.2f}",
-        }
-if __name__=="__main__":
-    parser = argparse.ArgumentParser()
-    # Inference configuration.
-    parser.add_argument('--submodel', type=str, default="DrugGEN", help="Chose model subtype: DrugGEN, NoTarget", choices=['DrugGEN', 'NoTarget'])
-    parser.add_argument('--inference_model', type=str, help="Path to the model for inference")
-    parser.add_argument('--sample_num', type=int, default=100, help='inference samples')
-    parser.add_argument('--correct', type=str2bool, default=False, help='Correct smiles')
-    # Data configuration.
-    parser.add_argument('--inf_dataset_file', type=str, default='chembl45_test.pt')
-    parser.add_argument('--inf_raw_file', type=str, default='data/chembl_test.smi')
-    parser.add_argument('--inf_batch_size', type=int, default=1, help='Batch size for inference')
-    parser.add_argument('--mol_data_dir', type=str, default='data')
-    parser.add_argument('--features', type=str2bool, default=False, help='features dimension for nodes')
-    # Model configuration.
-    parser.add_argument('--act', type=str, default="relu", help="Activation function for the model.", choices=['relu', 'tanh', 'leaky', 'sigmoid'])
-    parser.add_argument('--max_atom', type=int, default=45, help='Max atom number for molecules must be specified.')
-    parser.add_argument('--dim', type=int, default=128, help='Dimension of the Transformer Encoder model for the GAN.')
-    parser.add_argument('--depth', type=int, default=1, help='Depth of the Transformer model from the GAN.')
-    parser.add_argument('--heads', type=int, default=8, help='Number of heads for the MultiHeadAttention module from the GAN.')
-    parser.add_argument('--mlp_ratio', type=int, default=3, help='MLP ratio for the Transformer.')
-    parser.add_argument('--dropout', type=float, default=0., help='dropout rate')
-    # Seed configuration.
-    parser.add_argument('--set_seed', type=bool, default=False, help='set seed for reproducibility')
-    parser.add_argument('--seed', type=int, default=1, help='seed for reproducibility')
-    config = parser.parse_args()
-    inference = Inference(config)
-    inference.inference()