import gradio as gr import os import copy import os import torch import time from argparse import ArgumentParser, Namespace, FileType from rdkit.Chem import RemoveHs from functools import partial import numpy as np import pandas as pd from rdkit import RDLogger from rdkit.Chem import MolFromSmiles, AddHs from torch_geometric.loader import DataLoader import yaml from datasets.process_mols import ( read_molecule, generate_conformer, write_mol_with_coords, ) from datasets.pdbbind import PDBBind from utils.diffusion_utils import t_to_sigma as t_to_sigma_compl, get_t_schedule from utils.sampling import randomize_position, sampling from utils.utils import get_model from utils.visualise import PDBFile from tqdm import tqdm from datasets.esm_embedding_preparation import esm_embedding_prep import subprocess device = torch.device("cuda" if torch.cuda.is_available() else "cpu") with open(f"workdir/paper_score_model/model_parameters.yml") as f: score_model_args = Namespace(**yaml.full_load(f)) with open(f"workdir/paper_confidence_model/model_parameters.yml") as f: confidence_args = Namespace(**yaml.full_load(f)) t_to_sigma = partial(t_to_sigma_compl, args=score_model_args) model = get_model(score_model_args, device, t_to_sigma=t_to_sigma, no_parallel=True) state_dict = torch.load( f"workdir/paper_score_model/best_ema_inference_epoch_model.pt", map_location=torch.device("cpu"), ) model.load_state_dict(state_dict, strict=True) model = model.to(device) model.eval() confidence_model = get_model( confidence_args, device, t_to_sigma=t_to_sigma, no_parallel=True, confidence_mode=True, ) state_dict = torch.load( f"workdir/paper_confidence_model/best_model_epoch75.pt", map_location=torch.device("cpu"), ) confidence_model.load_state_dict(state_dict, strict=True) confidence_model = confidence_model.to(device) confidence_model.eval() tr_schedule = get_t_schedule(inference_steps=10) rot_schedule = tr_schedule tor_schedule = tr_schedule print("common t schedule", tr_schedule) failures, skipped, confidences_list, names_list, run_times, min_self_distances_list = ( 0, 0, [], [], [], [], ) N = 10 def get_pdb(pdb_code="", filepath=""): if pdb_code is None or pdb_code == "": try: return filepath.name except AttributeError as e: return None else: os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb") return f"{pdb_code}.pdb" def get_ligand(smiles="", filepath=""): if smiles is None or smiles == "": try: return filepath.name except AttributeError as e: return None else: return smiles def read_mol(molpath): with open(molpath, "r") as fp: lines = fp.readlines() mol = "" for l in lines: mol += l return mol def molecule(input_pdb, ligand_pdb): structure = read_mol(input_pdb) mol = read_mol(ligand_pdb) x = ( """
""" ) return f"""""" def esm(protein_path, out_file): esm_embedding_prep(out_file, protein_path) # create args object with defaults os.environ["HOME"] = "esm/model_weights" subprocess.call( f"python esm/scripts/extract.py esm2_t33_650M_UR50D {out_file} data/esm2_output --repr_layers 33 --include per_tok", shell=True, ) def update(inp, file, ligand_inp, ligand_file): pdb_path = get_pdb(inp, file) ligand_path = get_ligand(ligand_inp, ligand_file) esm( pdb_path, f"data/{os.path.basename(pdb_path)}_prepared_for_esm.fasta", ) protein_path_list = [pdb_path] ligand_descriptions = [ligand_path] no_random = False ode = False no_final_step_noise = False out_dir = "results/test" test_dataset = PDBBind( transform=None, root="", protein_path_list=protein_path_list, ligand_descriptions=ligand_descriptions, receptor_radius=score_model_args.receptor_radius, cache_path="data/cache", remove_hs=score_model_args.remove_hs, max_lig_size=None, c_alpha_max_neighbors=score_model_args.c_alpha_max_neighbors, matching=False, keep_original=False, popsize=score_model_args.matching_popsize, maxiter=score_model_args.matching_maxiter, all_atoms=score_model_args.all_atoms, atom_radius=score_model_args.atom_radius, atom_max_neighbors=score_model_args.atom_max_neighbors, esm_embeddings_path="data/esm2_output", require_ligand=True, num_workers=1, keep_local_structures=False, ) test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False) confidence_test_dataset = PDBBind( transform=None, root="", protein_path_list=protein_path_list, ligand_descriptions=ligand_descriptions, receptor_radius=confidence_args.receptor_radius, cache_path="data/cache", remove_hs=confidence_args.remove_hs, max_lig_size=None, c_alpha_max_neighbors=confidence_args.c_alpha_max_neighbors, matching=False, keep_original=False, popsize=confidence_args.matching_popsize, maxiter=confidence_args.matching_maxiter, all_atoms=confidence_args.all_atoms, atom_radius=confidence_args.atom_radius, atom_max_neighbors=confidence_args.atom_max_neighbors, esm_embeddings_path="data/esm2_output", require_ligand=True, num_workers=1, ) confidence_complex_dict = {d.name: d for d in confidence_test_dataset} for idx, orig_complex_graph in tqdm(enumerate(test_loader)): if ( confidence_model is not None and not ( confidence_args.use_original_model_cache or confidence_args.transfer_weights ) and orig_complex_graph.name[0] not in confidence_complex_dict.keys() ): skipped += 1 print( f"HAPPENING | The confidence dataset did not contain {orig_complex_graph.name[0]}. We are skipping this complex." ) continue try: data_list = [copy.deepcopy(orig_complex_graph) for _ in range(N)] randomize_position( data_list, score_model_args.no_torsion, no_random, score_model_args.tr_sigma_max, ) pdb = None lig = orig_complex_graph.mol[0] visualization_list = [] for graph in data_list: pdb = PDBFile(lig) pdb.add(lig, 0, 0) pdb.add( ( orig_complex_graph["ligand"].pos + orig_complex_graph.original_center ) .detach() .cpu(), 1, 0, ) pdb.add( (graph["ligand"].pos + graph.original_center).detach().cpu(), part=1, order=1, ) visualization_list.append(pdb) start_time = time.time() if confidence_model is not None and not ( confidence_args.use_original_model_cache or confidence_args.transfer_weights ): confidence_data_list = [ copy.deepcopy(confidence_complex_dict[orig_complex_graph.name[0]]) for _ in range(N) ] else: confidence_data_list = None data_list, confidence = sampling( data_list=data_list, model=model, inference_steps=10, tr_schedule=tr_schedule, rot_schedule=rot_schedule, tor_schedule=tor_schedule, device=device, t_to_sigma=t_to_sigma, model_args=score_model_args, no_random=no_random, ode=ode, visualization_list=visualization_list, confidence_model=confidence_model, confidence_data_list=confidence_data_list, confidence_model_args=confidence_args, batch_size=1, no_final_step_noise=no_final_step_noise, ) ligand_pos = np.asarray( [ complex_graph["ligand"].pos.cpu().numpy() + orig_complex_graph.original_center.cpu().numpy() for complex_graph in data_list ] ) run_times.append(time.time() - start_time) if confidence is not None and isinstance( confidence_args.rmsd_classification_cutoff, list ): confidence = confidence[:, 0] if confidence is not None: confidence = confidence.cpu().numpy() re_order = np.argsort(confidence)[::-1] confidence = confidence[re_order] confidences_list.append(confidence) ligand_pos = ligand_pos[re_order] write_dir = ( f'{out_dir}/index{idx}_{data_list[0]["name"][0].replace("/","-")}' ) os.makedirs(write_dir, exist_ok=True) for rank, pos in enumerate(ligand_pos): mol_pred = copy.deepcopy(lig) if score_model_args.remove_hs: mol_pred = RemoveHs(mol_pred) if rank == 0: write_mol_with_coords( mol_pred, pos, os.path.join(write_dir, f"rank{rank+1}.sdf") ) write_mol_with_coords( mol_pred, pos, os.path.join( write_dir, f"rank{rank+1}_confidence{confidence[rank]:.2f}.sdf" ), ) self_distances = np.linalg.norm( ligand_pos[:, :, None, :] - ligand_pos[:, None, :, :], axis=-1 ) self_distances = np.where( np.eye(self_distances.shape[2]), np.inf, self_distances ) min_self_distances_list.append(np.min(self_distances, axis=(1, 2))) filenames = [] if confidence is not None: for rank, batch_idx in enumerate(re_order): visualization_list[batch_idx].write( os.path.join(write_dir, f"rank{rank+1}_reverseprocess.pdb") ) filenames.append( os.path.join(write_dir, f"rank{rank+1}_reverseprocess.pdb") ) else: for rank, batch_idx in enumerate(ligand_pos): visualization_list[batch_idx].write( os.path.join(write_dir, f"rank{rank+1}_reverseprocess.pdb") ) filenames.append( os.path.join(write_dir, f"rank{rank+1}_reverseprocess.pdb") ) names_list.append(orig_complex_graph.name[0]) except Exception as e: print("Failed on", orig_complex_graph["name"], e) failures += 1 return None labels = [f"rank {i+1}" for i in range(len(filenames))] return ( molecule(pdb_path, filenames[0]), gr.Dropdown.update(choices=labels, value="rank 1"), filenames, pdb_path, ) def updateView(out, filenames, pdb): i = int(out.replace("rank", "")) return molecule(pdb, filenames[i]) demo = gr.Blocks() with demo: gr.Markdown("# DiffDock") gr.Markdown( ">**DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking**, Corso, Gabriele and Stärk, Hannes and Jing, Bowen and Barzilay, Regina and Jaakkola, Tommi, arXiv:2210.01776 [GitHub](https://github.com/gcorso/diffdock)" ) gr.Markdown("Runs the diffusion model `10` times with `10` inference steps") with gr.Box(): with gr.Row(): with gr.Column(): gr.Markdown("## Protein") inp = gr.Textbox( placeholder="PDB Code or upload file below", label="Input structure" ) file = gr.File(file_count="single", label="Input PDB") with gr.Column(): gr.Markdown("## Ligand") ligand_inp = gr.Textbox( placeholder="Provide SMILES input or upload mol2/sdf file below", label="SMILES string", ) ligand_file = gr.File(file_count="single", label="Input Ligand") btn = gr.Button("Run predictions") gr.Markdown("## Output") pdb = gr.Variable() filenames = gr.Variable() out = gr.Dropdown(interactive=True, label="Ranked samples") mol = gr.HTML() gr.Examples( [ [ None, "examples/1a46_protein_processed.pdb", None, "examples/1a46_ligand.sdf", ] ], [inp, file, ligand_inp, ligand_file], [mol, out], # cache_examples=True, ) btn.click( fn=update, inputs=[inp, file, ligand_inp, ligand_file], outputs=[mol, out, filenames, pdb], ) out.change(fn=updateView, inputs=[out, filenames, pdb], outputs=mol) demo.launch()