import gradio as gr import os import copy import os import torch import subprocess 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 import sys import csv csv.field_size_limit(sys.maxsize) print(torch.__version__) os.makedirs("data/esm2_output", exist_ok=True) os.makedirs("results", exist_ok=True) 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)) import shutil 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() def get_pdb(pdb_code="", filepath=""): try: return filepath.name except AttributeError as e: if pdb_code is None or pdb_code == "": 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, original_ligand): structure = read_mol(input_pdb) mol = read_mol(ligand_pdb) try: ligand = read_mol(original_ligand.name) _, ext = os.path.splitext(original_ligand.name) lig_str_1 = """let original_ligand = `""" + ligand + """`""" lig_str_2 = f""" viewer.addModel( original_ligand, "{ext[1:]}" ); viewer.getModel(2).setStyle({{stick:{{colorscheme:"greenCarbon"}}}});""" except AttributeError as e: ligand = None lig_str_1 = "" lig_str_2 = "" x = ( """

Uploaded ligand position Predicted ligand position

""" ) return f"""""" import sys def esm(protein_path, out_file): print("running esm") 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, env=os.environ, ) def update(inp, file, ligand_inp, ligand_file, n_it, n_samples, actual_steps, no_final_step_noise): 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", ) tr_schedule = get_t_schedule(inference_steps=n_it) 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 = n_samples # number of samples to generate protein_path_list = [pdb_path] ligand_descriptions = [ligand_path] no_random = False ode = False no_final_step_noise = no_final_step_noise out_dir = "results/" 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=actual_steps, 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) confidences = [] 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") ) confidences.append(confidence[rank]) 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 # zip outputs zippath = shutil.make_archive( os.path.join("results", os.path.basename(pdb_path)), "zip", write_dir ) print("Zipped outputs to", zippath) labels = [ f"rank {i+1}, confidence {confidences[i]:.2f}" for i in range(len(filenames)) ] torch.cuda.empty_cache() return ( molecule(pdb_path, filenames[0], ligand_file), gr.Dropdown.update(choices=labels, value=labels[0]), filenames, pdb_path, zippath, ) def updateView(out, filenames, pdb, ligand_file): print("updating view") i = out # int(out.replace("rank", "")) print(i) i = int(i.split(",")[0].replace("rank", "")) - 1 return molecule(pdb, filenames[i], ligand_file) 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("") 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") n_it = gr.Slider(value=20, minimum=10, maximum=40, label="Number of inference steps", step=1 ) actual_steps = gr.Slider(value=18, minimum=10, maximum=40, label="Number of actual inference steps", step=1 ) n_samples = gr.Slider(value=40, minimum=10, maximum=40, label="Number of samples", step=1 ) no_final_step_noise = gr.Checkbox(value=True,label="No final step noise" ) 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() output_file = gr.File(file_count="single", label="Output files") gr.Examples( [ [ "6w70", "examples/6w70.pdb", "COc1ccc(cc1)n2c3c(c(n2)C(=O)N)CCN(C3=O)c4ccc(cc4)N5CCCCC5=O", "examples/6w70_ligand.sdf", 20, 40, 18, True ], [ "6moa", "examples/6moa_protein_processed.pdb", "", "examples/6moa_ligand.sdf", 20, 40, 18, True ], [ "", "examples/6o5u_protein_processed.pdb", "", "examples/6o5u_ligand.sdf", 20, 40, 18, True ], [ "", "examples/6o5u_protein_processed.pdb", "[NH3+]C[C@H]1O[C@H](O[C@@H]2[C@@H]([NH3+])C[C@H]([C@@H]([C@H]2O)O[C@H]2O[C@H](CO)[C@H]([C@@H]([C@H]2O)[NH3+])O)[NH3+])[C@@H]([C@H]([C@@H]1O)O)O", "examples/6o5u_ligand.sdf", 20, 40, 18, True ], [ "", "examples/6o5u_protein_processed.pdb", "", "examples/6o5u_ligand.sdf", 20, 40, 18, True ], [ "", "examples/6ahs_protein_processed.pdb", "", "examples/6ahs_ligand.sdf", 20, 40, 18, True ], ], [inp, file, ligand_inp, ligand_file, n_it, n_samples, actual_steps, no_final_step_noise], [mol, out, filenames, pdb, output_file], # fn=update, # cache_examples=True, ) btn.click( fn=update, inputs=[inp, file, ligand_inp, ligand_file, n_it, n_samples, actual_steps, no_final_step_noise], outputs=[mol, out, filenames, pdb, output_file], ) out.change(fn=updateView, inputs=[out, filenames, pdb, ligand_file], outputs=mol) demo.launch()