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' 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=32 depth=1 heads=8 mlp_ratio=3 dropout=0. log_sample_step=1000 set_seed=True seed=10 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("[![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. - **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)"] ) 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()