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| 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 | |
| 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' | |
| inference_model="experiments/models/DrugGEN" | |
| sample_dir='experiments/samples' | |
| result_dir="experiments/tboard_output" | |
| dataset_file="chembl45_train.pt" | |
| drug_dataset_file="akt_train.pt" | |
| raw_file='data/chembl_train.smi' | |
| drug_raw_file="data/akt_train.smi" | |
| 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" | |
| 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(), | |
| } | |
| def function(model_name: str, mol_num: int, seed: int) -> tuple[PIL.Image, pd.DataFrame, str]: | |
| ''' | |
| Returns: | |
| image, score_df, file path | |
| ''' | |
| config = model_configs[model_name] | |
| config.inference_sample_num = mol_num | |
| config.seed = seed | |
| 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' | |
| import os | |
| 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") | |
| rng = random.Random(seed) | |
| selected_molecules = rng.choices(generated_molecule_list,k=12) | |
| selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules] | |
| 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("[](https://arxiv.org/abs/2302.07868)") | |
| gr.Markdown("[](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. | |
| - **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", "NoTarget"), | |
| value="DrugGEN", | |
| label="Select a model to make inference", | |
| info=" DrugGEN model design molecules to target the AKT1 protein" | |
| ) | |
| num_molecules = gr.Number( | |
| label="Number of molecules to generate", | |
| precision=0, # integer input | |
| minimum=1, | |
| value=1000, | |
| maximum=10_000, | |
| ) | |
| seed_num = gr.Number( | |
| label="RNG seed value (can be used for reproducibility):", | |
| precision=0, # integer input | |
| minimum=0, | |
| value=42, | |
| ) | |
| 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)", "Novelty (AKT)", "MaxLen", "MeanAtomType", "SNN (ChEMBL)", "SNN (AKT)"], | |
| ) | |
| file_download = gr.File( | |
| label="Click to download generated molecules", | |
| ) | |
| image_output = gr.Image( | |
| label="Structures of randomly selected 12 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() | |