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| import os | |
| import re | |
| import logging | |
| import pandas as pd | |
| from omegaconf import OmegaConf | |
| import gradio as gr | |
| from illuminate import Illuminate | |
| import matplotlib.pyplot as plt | |
| from rdkit import Chem | |
| from rdkit.Chem import Draw | |
| def MolsMatrixToGridImage(mols, legends, filename): | |
| img = Draw.MolsToGridImage(mols, molsPerRow=5, subImgSize=(400,400), legends=legends) | |
| img.save(filename) | |
| return img | |
| def launch_illumination(target, representation, surrogate, acquisition, ranges, generations_max, function_calls_max, structural_filters): | |
| config = { | |
| 'controller': { | |
| 'max_generations': generations_max, | |
| 'max_fitness_calls': function_calls_max | |
| }, | |
| 'archive': { | |
| 'name': 'Troglitazone', | |
| 'size': 150, | |
| 'accuracy': 25000 | |
| }, | |
| 'descriptor': { | |
| 'properties': [ | |
| 'Descriptors.ExactMolWt', | |
| 'Descriptors.MolLogP', | |
| 'Descriptors.TPSA', | |
| 'Crippen.MolMR' | |
| ], | |
| 'ranges': ranges | |
| }, | |
| 'fitness': { | |
| 'type': 'Fingerprint', | |
| 'target': target, | |
| 'representation': representation | |
| }, | |
| 'arbiter': { | |
| 'rules': [rule_set for rule_set in structural_filters] | |
| }, | |
| 'generator': { | |
| 'batch_size': 40, | |
| 'initial_size': 40, | |
| 'mutation_data': 'data/smarts/mutation_collection.tsv', | |
| 'initial_data': 'data/smiles/guacamol_intitial_rediscovery_troglitazone.smi' | |
| }, | |
| 'surrogate': { | |
| 'type': "Fingerprint", | |
| 'representation': surrogate, | |
| }, | |
| 'acquisition': { | |
| 'type': acquisition, | |
| 'beta': 0.3 | |
| } | |
| } | |
| log = logging.getLogger(__name__) | |
| log.info(OmegaConf.to_yaml(config)) | |
| current_instance = Illuminate(OmegaConf.create(config)) | |
| current_instance() | |
| stats_file = pd.read_csv("statistics.csv") | |
| molecules_file = pd.read_csv("molecules.csv") | |
| # files_in_directory = os.listdir('.') | |
| # pattern = re.compile(r'archive_(\d+)\.csv') | |
| # archive_files = [f for f in files_in_directory if pattern.match(f)] | |
| # archive_numbers = [int(pattern.search(f).group(1)) for f in archive_files] | |
| # archive_file = pd.read_csv(f'archive_{max(archive_numbers)}.csv') | |
| # csv_files = [file for file in files_in_directory if file.endswith('.csv')] | |
| # for csv_file in csv_files: | |
| # if os.path.isfile(csv_file): | |
| # os.remove(csv_file) | |
| top_molecules = molecules_file.nlargest(10, 'fitness') | |
| top_smiles = top_molecules['smiles'].tolist() | |
| top_fitness = top_molecules['fitness'].tolist() | |
| top_mols = [Chem.MolFromSmiles(smile) for smile in top_smiles] | |
| top_legends = [f'Similarity: {score:.5f}' for score in top_fitness] | |
| image = MolsMatrixToGridImage(mols=top_mols, legends=top_legends, filename='top_molecules_grid.png') | |
| return image, stats_file, molecules_file | |
| def validate_and_process(target, representation, surrogate, acquisition, exact_mol_wt_min, exact_mol_wt_max, mol_log_p_min, mol_log_p_max, tpsa_min, tpsa_max, mol_mr_min, mol_mr_max, generations_max, function_calls_max, structural_filters): | |
| # Ensure min is less than max for each range | |
| exact_mol_wt_range = sorted([exact_mol_wt_min, exact_mol_wt_max]) | |
| mol_log_p_range = sorted([mol_log_p_min, mol_log_p_max]) | |
| tpsa_range = sorted([tpsa_min, tpsa_max]) | |
| mol_mr_range = sorted([mol_mr_min, mol_mr_max]) | |
| ranges = [ | |
| exact_mol_wt_range, | |
| mol_log_p_range, | |
| tpsa_range, | |
| mol_mr_range | |
| ] | |
| image, stats_file, molecules_file = launch_illumination(target, representation, surrogate, acquisition, ranges, generations_max, function_calls_max, structural_filters) | |
| return image | |
| def gradio_interface(): | |
| with gr.Blocks() as demo: | |
| representation_options = ["ECFP4", "ECFP6", "FCFP4", "FCFP6"] | |
| surrogate_options = ["ECFP4", "ECFP6", "FCFP4", "FCFP6", "RDFP", "APFP", "TTFP"] | |
| acquisition_options = ["Mean", "UCB", "EI", "logEI"] | |
| target = gr.Textbox(label="Target (SMILES)", value="O=C1NC(=O)SC1Cc4ccc(OCC3(Oc2c(c(c(O)c(c2CC3)C)C)C)C)cc4") | |
| with gr.Row(): | |
| generations_max = gr.Slider(minimum=0, maximum=150, value=1, step=1, label="Generations") | |
| function_calls_max = gr.Slider(minimum=0, maximum=15000, value=5000, step=100, label="Function Calls") | |
| structural_filters = gr.CheckboxGroup(["BMS", "Dundee", "Glaxo", "Inpharmatica", "LINT", "MLSMR", "PAINS", "SureChEMBL"], label="Structural Filters") | |
| with gr.Row(): | |
| representation = gr.Dropdown(choices=representation_options, value="ECFP4", label="Fitness Representation") | |
| surrogate = gr.Dropdown(choices=surrogate_options, value="ECFP4", label="Surrogate Representation") | |
| acquisition = gr.Dropdown(choices=acquisition_options, value="Mean", label="Acquisition Function") | |
| with gr.Accordion("Physicochemical Descriptors", open=False): | |
| with gr.Row(): | |
| exact_mol_wt_min = gr.Slider(minimum=0, maximum=885, value=225, step=1, label="Minimum Molecular Weight") | |
| exact_mol_wt_max = gr.Slider(minimum=0, maximum=885, value=555, step=1, label="Maximum Molecular Weight") | |
| with gr.Row(): | |
| mol_log_p_min = gr.Slider(minimum=-4, maximum=8, value=-0.5, step=0.1, label="Minimum Log(P)") | |
| mol_log_p_max = gr.Slider(minimum=-4, maximum=8, value=5.5, step=0.1, label="Maximum Log(P)") | |
| with gr.Row(): | |
| tpsa_min = gr.Slider(minimum=0, maximum=250, value=0, step=1, label="Minimum TPSA") | |
| tpsa_max = gr.Slider(minimum=0, maximum=250, value=140, step=1, label="Maximum TPSA") | |
| with gr.Row(): | |
| mol_mr_min = gr.Slider(minimum=0, maximum=250, value=40, step=1, label="Minimum Molecular Refractivity") | |
| mol_mr_max = gr.Slider(minimum=0, maximum=250, value=130, step=1, label="Maximum Molecular Refractivity") | |
| submit_btn = gr.Button("Submit") | |
| output_image = gr.Image(label="Top Molecules") | |
| gr.DownloadButton(label=f"Download Optimisation History", value="./statistics.csv", visible=True) | |
| gr.DownloadButton(label=f"Download Output Molecules", value="./molecules.csv", visible=True) | |
| submit_btn.click( | |
| validate_and_process, | |
| inputs=[ | |
| target, | |
| representation, | |
| surrogate, | |
| acquisition, | |
| exact_mol_wt_min, | |
| exact_mol_wt_max, | |
| mol_log_p_min, | |
| mol_log_p_max, | |
| tpsa_min, | |
| tpsa_max, | |
| mol_mr_min, | |
| mol_mr_max, | |
| generations_max, | |
| function_calls_max, | |
| structural_filters, | |
| ], | |
| outputs=[output_image] | |
| ) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| gradio_interface() | |