import copy import os import torch from datasets.moad import MOAD from utils.gnina_utils import get_gnina_poses from utils.molecules_utils import get_symmetry_rmsd torch.multiprocessing.set_sharing_strategy('file_system') import resource rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) resource.setrlimit(resource.RLIMIT_NOFILE, (64000, rlimit[1])) import time from argparse import ArgumentParser, Namespace, FileType from datetime import datetime from functools import partial import numpy as np import wandb from rdkit import RDLogger from torch_geometric.loader import DataLoader from rdkit.Chem import RemoveAllHs 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, ExponentialMovingAverage from utils.visualise import PDBFile from tqdm import tqdm RDLogger.DisableLog('rdApp.*') import yaml import pickle def get_dataset(args, model_args, confidence=False): if args.dataset != 'moad': dataset = PDBBind(transform=None, root=args.data_dir, limit_complexes=args.limit_complexes, dataset=args.dataset, chain_cutoff=args.chain_cutoff, receptor_radius=model_args.receptor_radius, cache_path=args.cache_path, split_path=args.split_path, remove_hs=model_args.remove_hs, max_lig_size=None, c_alpha_max_neighbors=model_args.c_alpha_max_neighbors, matching=not model_args.no_torsion, keep_original=True, popsize=args.matching_popsize, maxiter=args.matching_maxiter, all_atoms=model_args.all_atoms if 'all_atoms' in model_args else False, atom_radius=model_args.atom_radius if 'all_atoms' in model_args else None, atom_max_neighbors=model_args.atom_max_neighbors if 'all_atoms' in model_args else None, esm_embeddings_path=args.esm_embeddings_path, require_ligand=True, num_workers=args.num_workers, protein_file=args.protein_file, ligand_file=args.ligand_file, knn_only_graph=True if not hasattr(args, 'not_knn_only_graph') else not args.not_knn_only_graph, include_miscellaneous_atoms=False if not hasattr(args, 'include_miscellaneous_atoms') else args.include_miscellaneous_atoms, num_conformers=args.samples_per_complex if args.resample_rdkit and not confidence else 1) else: dataset = MOAD(transform=None, root=args.data_dir, limit_complexes=args.limit_complexes, chain_cutoff=args.chain_cutoff, receptor_radius=model_args.receptor_radius, cache_path=args.cache_path, split=args.split, remove_hs=model_args.remove_hs, max_lig_size=None, c_alpha_max_neighbors=model_args.c_alpha_max_neighbors, matching=not model_args.no_torsion, keep_original=True, popsize=args.matching_popsize, maxiter=args.matching_maxiter, all_atoms=model_args.all_atoms if 'all_atoms' in model_args else False, atom_radius=model_args.atom_radius if 'all_atoms' in model_args else None, atom_max_neighbors=model_args.atom_max_neighbors if 'all_atoms' in model_args else None, esm_embeddings_path=args.esm_embeddings_path, esm_embeddings_sequences_path=args.moad_esm_embeddings_sequences_path, require_ligand=True, num_workers=args.num_workers, knn_only_graph=True if not hasattr(args, 'not_knn_only_graph') else not args.not_knn_only_graph, include_miscellaneous_atoms=False if not hasattr(args, 'include_miscellaneous_atoms') else args.include_miscellaneous_atoms, num_conformers=args.samples_per_complex if args.resample_rdkit and not confidence else 1, unroll_clusters=args.unroll_clusters, remove_pdbbind=args.remove_pdbbind, min_ligand_size=args.min_ligand_size, max_receptor_size=args.max_receptor_size, remove_promiscuous_targets=args.remove_promiscuous_targets, no_randomness=True, skip_matching=args.skip_matching) return dataset if __name__ == '__main__': cache_name = datetime.now().strftime('date%d-%m_time%H-%M-%S.%f') parser = ArgumentParser() parser.add_argument('--config', type=FileType(mode='r'), default=None) parser.add_argument('--model_dir', type=str, default='workdir/test_score', help='Path to folder with trained score model and hyperparameters') parser.add_argument('--ckpt', type=str, default='best_ema_inference_epoch_model.pt', help='Checkpoint to use inside the folder') parser.add_argument('--confidence_model_dir', type=str, default=None, help='Path to folder with trained confidence model and hyperparameters') parser.add_argument('--confidence_ckpt', type=str, default='best_model_epoch75.pt', help='Checkpoint to use inside the folder') parser.add_argument('--num_cpu', type=int, default=None, help='if this is a number instead of none, the max number of cpus used by torch will be set to this.') parser.add_argument('--run_name', type=str, default='test', help='') parser.add_argument('--project', type=str, default='ligbind_inf', help='') parser.add_argument('--out_dir', type=str, default=None, help='Where to save results to') parser.add_argument('--batch_size', type=int, default=40, help='Number of poses to sample in parallel') parser.add_argument('--old_score_model', action='store_true', default=False, help='') parser.add_argument('--old_confidence_model', action='store_true', default=True, help='') parser.add_argument('--matching_popsize', type=int, default=40, help='Differential evolution popsize parameter in matching') parser.add_argument('--matching_maxiter', type=int, default=40, help='Differential evolution maxiter parameter in matching') parser.add_argument('--esm_embeddings_path', type=str, default=None, help='If this is set then the LM embeddings at that path will be used for the receptor features') parser.add_argument('--moad_esm_embeddings_sequences_path', type=str, default=None, help='') parser.add_argument('--chain_cutoff', type=float, default=None, help='Cutoff of the chains from the ligand') # TODO remove parser.add_argument('--save_complexes', action='store_true', default=False, help='Save generated complex graphs') parser.add_argument('--complexes_save_path', type=str, default=None, help='') parser.add_argument('--dataset', type=str, default='moad', help='') parser.add_argument('--cache_path', type=str, default='data/cache', help='Folder from where to load/restore cached dataset') parser.add_argument('--data_dir', type=str, default='../../ligbind/data/BindingMOAD_2020_ab_processed_biounit/', help='Folder containing original structures') parser.add_argument('--split_path', type=str, default='data/BindingMOAD_2020_ab_processed/splits/val.txt', help='Path of file defining the split') parser.add_argument('--no_model', action='store_true', default=False, help='Whether to return seed conformer without running model') parser.add_argument('--no_random', action='store_true', default=False, help='Whether to add randomness in diffusion steps') parser.add_argument('--no_final_step_noise', action='store_true', default=False, help='Whether to add noise after the final step') parser.add_argument('--ode', action='store_true', default=False, help='Whether to run the probability flow ODE') parser.add_argument('--wandb', action='store_true', default=False, help='') # TODO remove parser.add_argument('--inference_steps', type=int, default=40, help='Number of denoising steps') parser.add_argument('--limit_complexes', type=int, default=0, help='Limit to the number of complexes') parser.add_argument('--num_workers', type=int, default=1, help='Number of workers for dataset creation') parser.add_argument('--tqdm', action='store_true', default=False, help='Whether to show progress bar') parser.add_argument('--save_visualisation', action='store_true', default=True, help='Whether to save visualizations') parser.add_argument('--samples_per_complex', type=int, default=4, help='Number of poses to sample for each complex') parser.add_argument('--resample_rdkit', action='store_true', default=False, help='') parser.add_argument('--skip_matching', action='store_true', default=False, help='') parser.add_argument('--sigma_schedule', type=str, default='expbeta', help='Schedule type, no other options') parser.add_argument('--inf_sched_alpha', type=float, default=1, help='Alpha parameter of beta distribution for t sched') parser.add_argument('--inf_sched_beta', type=float, default=1, help='Beta parameter of beta distribution for t sched') parser.add_argument('--pocket_knowledge', action='store_true', default=False, help='') parser.add_argument('--no_random_pocket', action='store_true', default=False, help='') parser.add_argument('--pocket_tr_max', type=float, default=3, help='') parser.add_argument('--pocket_cutoff', type=float, default=5, help='') parser.add_argument('--actual_steps', type=int, default=None, help='') parser.add_argument('--restrict_cpu', action='store_true', default=False, help='') parser.add_argument('--force_fixed_center_conv', action='store_true', default=False, help='') parser.add_argument('--protein_file', type=str, default='protein_processed', help='') parser.add_argument('--unroll_clusters', action='store_true', default=True, help='') parser.add_argument('--ligand_file', type=str, default='ligand', help='') parser.add_argument('--remove_pdbbind', action='store_true', default=False, help='') parser.add_argument('--split', type=str, default='val', help='') parser.add_argument('--limit_failures', type=float, default=5, help='') parser.add_argument('--min_ligand_size', type=float, default=0, help='') parser.add_argument('--max_receptor_size', type=float, default=None, help='') parser.add_argument('--remove_promiscuous_targets', type=float, default=None, help='') parser.add_argument('--initial_noise_std_proportion', type=float, default=-1.0, help='Initial noise std proportion') parser.add_argument('--choose_residue', action='store_true', default=False, help='') parser.add_argument('--temp_sampling_tr', type=float, default=1.0) parser.add_argument('--temp_psi_tr', type=float, default=0.0) parser.add_argument('--temp_sigma_data_tr', type=float, default=0.5) parser.add_argument('--temp_sampling_rot', type=float, default=1.0) parser.add_argument('--temp_psi_rot', type=float, default=0.0) parser.add_argument('--temp_sigma_data_rot', type=float, default=0.5) parser.add_argument('--temp_sampling_tor', type=float, default=1.0) parser.add_argument('--temp_psi_tor', type=float, default=0.0) parser.add_argument('--temp_sigma_data_tor', type=float, default=0.5) parser.add_argument('--gnina_minimize', action='store_true', default=False, help='') parser.add_argument('--gnina_path', type=str, default='gnina', help='') parser.add_argument('--gnina_log_file', type=str, default='gnina_log.txt', help='') # To redirect gnina subprocesses stdouts from the terminal window parser.add_argument('--gnina_full_dock', action='store_true', default=False, help='') parser.add_argument('--save_gnina_metrics', action='store_true', default=False, help='') parser.add_argument('--gnina_autobox_add', type=float, default=4.0) parser.add_argument('--gnina_poses_to_optimize', type=int, default=1) args = parser.parse_args() if args.config: config_dict = yaml.load(args.config, Loader=yaml.FullLoader) arg_dict = args.__dict__ for key, value in config_dict.items(): if isinstance(value, list): for v in value: arg_dict[key].append(v) else: arg_dict[key] = value if args.restrict_cpu: threads = 16 os.environ["OMP_NUM_THREADS"] = str(threads) # export OMP_NUM_THREADS=4 os.environ["OPENBLAS_NUM_THREADS"] = str(threads) # export OPENBLAS_NUM_THREADS=4 os.environ["MKL_NUM_THREADS"] = str(threads) # export MKL_NUM_THREADS=6 os.environ["VECLIB_MAXIMUM_THREADS"] = str(threads) # export VECLIB_MAXIMUM_THREADS=4 os.environ["NUMEXPR_NUM_THREADS"] = str(threads) # export NUMEXPR_NUM_THREADS=6 os.environ["CUDA_VISIBLE_DEVICES"] = "" torch.set_num_threads(threads) if args.out_dir is None: args.out_dir = f'inference_out_dir_not_specified/{args.run_name}' os.makedirs(args.out_dir, exist_ok=True) with open(f'{args.model_dir}/model_parameters.yml') as f: score_model_args = Namespace(**yaml.full_load(f)) if not hasattr(score_model_args, 'separate_noise_schedule'): # exists for compatibility with old runs that did not have the attribute score_model_args.separate_noise_schedule = False if not hasattr(score_model_args, 'lm_embeddings_path'): score_model_args.lm_embeddings_path = None if not hasattr(score_model_args, 'tr_only_confidence'): score_model_args.tr_only_confidence = True if not hasattr(score_model_args, 'high_confidence_threshold'): score_model_args.high_confidence_threshold = 0.0 if not hasattr(score_model_args, 'include_confidence_prediction'): score_model_args.include_confidence_prediction = False if not hasattr(score_model_args, 'confidence_weight'): score_model_args.confidence_weight = 1 if not hasattr(score_model_args, 'asyncronous_noise_schedule'): score_model_args.asyncronous_noise_schedule = False if not hasattr(score_model_args, 'correct_torsion_sigmas'): score_model_args.correct_torsion_sigmas = False if not hasattr(score_model_args, 'esm_embeddings_path'): score_model_args.esm_embeddings_path = None if args.force_fixed_center_conv: score_model_args.not_fixed_center_conv = False if args.confidence_model_dir is not None: with open(f'{args.confidence_model_dir}/model_parameters.yml') as f: confidence_args = Namespace(**yaml.full_load(f)) if not os.path.exists(confidence_args.original_model_dir): print("Path does not exist: ", confidence_args.original_model_dir) confidence_args.original_model_dir = os.path.join(*confidence_args.original_model_dir.split('/')[-2:]) print('instead trying path: ', confidence_args.original_model_dir) if not hasattr(confidence_args, 'use_original_model_cache'): confidence_args.use_original_model_cache = True if not hasattr(confidence_args, 'esm_embeddings_path'): confidence_args.esm_embeddings_path = None if not hasattr(confidence_args, 'num_classification_bins'): confidence_args.num_classification_bins = 2 if args.num_cpu is not None: torch.set_num_threads(args.num_cpu) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') test_dataset = get_dataset(args, score_model_args) test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False) if args.confidence_model_dir is not None: if not (confidence_args.use_original_model_cache or confidence_args.transfer_weights): # if the confidence model uses the same type of data as the original model then we do not need this dataset and can just use the complexes print('HAPPENING | confidence model uses different type of graphs than the score model. Loading (or creating if not existing) the data for the confidence model now.') confidence_test_dataset = get_dataset(args, confidence_args, confidence=True) confidence_complex_dict = {d.name: d for d in confidence_test_dataset} t_to_sigma = partial(t_to_sigma_compl, args=score_model_args) if not args.no_model: model = get_model(score_model_args, device, t_to_sigma=t_to_sigma, no_parallel=True, old=args.old_score_model) state_dict = torch.load(f'{args.model_dir}/{args.ckpt}', map_location=torch.device('cpu')) if args.ckpt == 'last_model.pt': model_state_dict = state_dict['model'] ema_weights_state = state_dict['ema_weights'] model.load_state_dict(model_state_dict, strict=True) ema_weights = ExponentialMovingAverage(model.parameters(), decay=score_model_args.ema_rate) ema_weights.load_state_dict(ema_weights_state, device=device) ema_weights.copy_to(model.parameters()) else: model.load_state_dict(state_dict, strict=True) model = model.to(device) model.eval() if args.confidence_model_dir is not None: if confidence_args.transfer_weights: with open(f'{confidence_args.original_model_dir}/model_parameters.yml') as f: confidence_model_args = Namespace(**yaml.full_load(f)) if not hasattr(confidence_model_args, 'separate_noise_schedule'): # exists for compatibility with old runs that did not have the # attribute confidence_model_args.separate_noise_schedule = False if not hasattr(confidence_model_args, 'lm_embeddings_path'): confidence_model_args.lm_embeddings_path = None if not hasattr(confidence_model_args, 'tr_only_confidence'): confidence_model_args.tr_only_confidence = True if not hasattr(confidence_model_args, 'high_confidence_threshold'): confidence_model_args.high_confidence_threshold = 0.0 if not hasattr(confidence_model_args, 'include_confidence_prediction'): confidence_model_args.include_confidence_prediction = False if not hasattr(confidence_model_args, 'confidence_dropout'): confidence_model_args.confidence_dropout = confidence_model_args.dropout if not hasattr(confidence_model_args, 'confidence_no_batchnorm'): confidence_model_args.confidence_no_batchnorm = False if not hasattr(confidence_model_args, 'confidence_weight'): confidence_model_args.confidence_weight = 1 if not hasattr(confidence_model_args, 'asyncronous_noise_schedule'): confidence_model_args.asyncronous_noise_schedule = False if not hasattr(confidence_model_args, 'correct_torsion_sigmas'): confidence_model_args.correct_torsion_sigmas = False if not hasattr(confidence_model_args, 'esm_embeddings_path'): confidence_model_args.esm_embeddings_path = None if not hasattr(confidence_model_args, 'not_fixed_knn_radius_graph'): confidence_model_args.not_fixed_knn_radius_graph = True if not hasattr(confidence_model_args, 'not_knn_only_graph'): confidence_model_args.not_knn_only_graph = True else: confidence_model_args = confidence_args confidence_model = get_model(confidence_model_args, device, t_to_sigma=t_to_sigma, no_parallel=True, confidence_mode=True, old=args.old_confidence_model) state_dict = torch.load(f'{args.confidence_model_dir}/{args.confidence_ckpt}', map_location=torch.device('cpu')) confidence_model.load_state_dict(state_dict, strict=True) confidence_model = confidence_model.to(device) confidence_model.eval() else: confidence_model = None confidence_args = None confidence_model_args = None if args.wandb: run = wandb.init( entity='', settings=wandb.Settings(start_method="fork"), project=args.project, name=args.run_name, config=args ) if args.pocket_knowledge and args.different_schedules: t_max = (np.log(args.pocket_tr_max) - np.log(score_model_args.tr_sigma_min)) / ( np.log(score_model_args.tr_sigma_max) - np.log(score_model_args.tr_sigma_min)) else: t_max = 1 tr_schedule = get_t_schedule(sigma_schedule=args.sigma_schedule, inference_steps=args.inference_steps, inf_sched_alpha=args.inf_sched_alpha, inf_sched_beta=args.inf_sched_beta, t_max=t_max) t_schedule = None rot_schedule = tr_schedule tor_schedule = tr_schedule print('common t schedule', tr_schedule) rmsds_list, obrmsds, centroid_distances_list, failures, skipped, min_cross_distances_list, base_min_cross_distances_list, confidences_list, names_list = [], [], [], 0, 0, [], [], [], [] run_times, min_self_distances_list, without_rec_overlap_list = [], [], [] gnina_rmsds_list, gnina_score_list = [], [] N = args.samples_per_complex #names_no_rec_overlap = read_strings_from_txt(f'data/splits/timesplit_test_no_rec_overlap') #names_no_rec_overlap = np.load("data/BindingMOAD_2020_processed/test_names_bootstrapping.npy") names_no_rec_overlap = [] print('Size of test dataset: ', len(test_dataset)) if args.save_complexes: sampled_complexes = {} if args.save_gnina_metrics: # key is complex_name, value is the gnina metrics for all samples gnina_metrics = {} for idx, orig_complex_graph in tqdm(enumerate(test_loader)): torch.cuda.empty_cache() 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 success = 0 bs = args.batch_size while 0 >= success > -args.limit_failures: try: data_list = [copy.deepcopy(orig_complex_graph) for _ in range(N)] if args.resample_rdkit: for i, g in enumerate(data_list): g['ligand'].pos = g['ligand'].pos[i] randomize_position(data_list, score_model_args.no_torsion, args.no_random or args.no_random_pocket, score_model_args.tr_sigma_max if not args.pocket_knowledge else args.pocket_tr_max, args.pocket_knowledge, args.pocket_cutoff, initial_noise_std_proportion=args.initial_noise_std_proportion, choose_residue=args.choose_residue) pdb = None if args.save_visualisation: visualization_list = [] for idx, graph in enumerate(data_list): lig = orig_complex_graph.mol[0] pdb = PDBFile(lig) pdb.add(lig, 0, 0) pdb.add(((orig_complex_graph['ligand'].pos if not args.resample_rdkit else orig_complex_graph['ligand'].pos[idx]) + 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) else: visualization_list = None start_time = time.time() if not args.no_model: 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=args.actual_steps if args.actual_steps is not None else args.inference_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=args.no_random, ode=args.ode, visualization_list=visualization_list, confidence_model=confidence_model, confidence_data_list=confidence_data_list, confidence_model_args=confidence_model_args, t_schedule=t_schedule, batch_size=bs, no_final_step_noise=args.no_final_step_noise, pivot=None, temp_sampling=[args.temp_sampling_tr, args.temp_sampling_rot, args.temp_sampling_tor], temp_psi=[args.temp_psi_tr, args.temp_psi_rot, args.temp_psi_tor], temp_sigma_data=[args.temp_sigma_data_tr, args.temp_sigma_data_rot, args.temp_sigma_data_tor]) run_times.append(time.time() - start_time) if score_model_args.no_torsion: orig_complex_graph['ligand'].orig_pos = (orig_complex_graph['ligand'].pos.cpu().numpy() + orig_complex_graph.original_center.cpu().numpy()) filterHs = torch.not_equal(data_list[0]['ligand'].x[:, 0], 0).cpu().numpy() if isinstance(orig_complex_graph['ligand'].orig_pos, list): # Same pair with multiple binding positions # print(f'Number of ground truth poses: {len(orig_complex_graph['ligand'].orig_pos)}') if args.dataset == 'moad' or args.dataset == 'posebusters': orig_ligand_pos = np.array([pos[filterHs] - orig_complex_graph.original_center.cpu().numpy() for pos in orig_complex_graph['ligand'].orig_pos[0]]) else: orig_ligand_pos = np.array([pos[filterHs] - orig_complex_graph.original_center.cpu().numpy() for pos in [orig_complex_graph['ligand'].orig_pos[0]]]) print('Found ', len(orig_ligand_pos), ' ground truth poses') else: print('default path') orig_ligand_pos = np.expand_dims( orig_complex_graph['ligand'].orig_pos[filterHs] - orig_complex_graph.original_center.cpu().numpy(), axis=0) ligand_pos = np.asarray( [complex_graph['ligand'].pos.cpu().numpy()[filterHs] for complex_graph in data_list]) # Use gnina to minimize energy for predicted ligands. if args.gnina_minimize: print('Running gnina on all predicted ligand positions for energy minimization.') gnina_rmsds, gnina_scores = [], [] lig = copy.deepcopy(orig_complex_graph.mol[0]) positions = np.asarray([complex_graph['ligand'].pos.cpu().numpy() for complex_graph in data_list]) conf = confidence if conf is not None and isinstance(confidence_args.rmsd_classification_cutoff, list): conf = conf[:, 0] if conf is not None: conf = conf.cpu().numpy() conf = np.nan_to_num(conf, nan=-1e-6) re_order = np.argsort(conf)[::-1] positions = positions[re_order] for pos in positions[:args.gnina_poses_to_optimize]: center = orig_complex_graph.original_center.cpu().numpy() gnina_ligand_pos, gnina_mol, gnina_score = get_gnina_poses(args, lig, pos, center, name=orig_complex_graph.name[0], folder=args.folder, gnina_path=args.gnina_path) # TODO set the right folder mol = RemoveAllHs(orig_complex_graph.mol[0]) rmsds = [] for i in range(len(orig_ligand_pos)): try: rmsd = get_symmetry_rmsd(mol, orig_ligand_pos[i], gnina_ligand_pos, gnina_mol) except Exception as e: print("Using non corrected RMSD because of the error:", e) rmsd = np.sqrt(((gnina_ligand_pos - orig_ligand_pos[i]) ** 2).sum(axis=1).mean(axis=0)) rmsds.append(rmsd) rmsds = np.asarray(rmsds) rmsd = np.min(rmsds, axis=0) gnina_rmsds.append(rmsd) gnina_scores.append(gnina_score) gnina_rmsds = np.asarray(gnina_rmsds) assert gnina_rmsds.shape == (args.gnina_poses_to_optimize,), str(gnina_rmsds.shape) + " " + str(args.gnina_poses_to_optimize) gnina_rmsds_list.append(gnina_rmsds) gnina_scores = np.asarray(gnina_scores) gnina_score_list.append(gnina_scores) mol = RemoveAllHs(orig_complex_graph.mol[0]) rmsds = [] for i in range(len(orig_ligand_pos)): try: rmsd = get_symmetry_rmsd(mol, orig_ligand_pos[i], [l for l in ligand_pos]) except Exception as e: print("Using non corrected RMSD because of the error:", e) rmsd = np.sqrt(((ligand_pos - orig_ligand_pos[i]) ** 2).sum(axis=2).mean(axis=1)) rmsds.append(rmsd) rmsds = np.asarray(rmsds) rmsd = np.min(rmsds, axis=0) centroid_distance = np.min(np.linalg.norm(ligand_pos.mean(axis=1)[None, :] - orig_ligand_pos.mean(axis=1)[:, None], axis=2), axis=0) 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() confidence = np.nan_to_num(confidence, nan=-1e-6) re_order = np.argsort(confidence)[::-1] print(orig_complex_graph['name'], ' rmsd', np.around(rmsd, 1)[re_order], ' centroid distance', np.around(centroid_distance, 1)[re_order], ' confidences ', np.around(confidence, 4)[re_order], (' gnina rmsd ' + str(np.around(gnina_rmsds, 1))) if args.gnina_minimize else '') confidences_list.append(confidence) else: print(orig_complex_graph['name'], ' rmsd', np.around(rmsd, 1), ' centroid distance', np.around(centroid_distance, 1)) centroid_distances_list.append(centroid_distance) 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))) if args.save_complexes: sampled_complexes[orig_complex_graph.name[0]] = data_list if args.save_visualisation: if confidence is not None: for rank, batch_idx in enumerate(re_order): visualization_list[batch_idx].write( f'{args.out_dir}/{data_list[batch_idx]["name"][0]}_{rank + 1}_{rmsd[batch_idx]:.1f}_{(confidence)[batch_idx]:.1f}.pdb') else: for rank, batch_idx in enumerate(np.argsort(rmsd)): visualization_list[batch_idx].write( f'{args.out_dir}/{data_list[batch_idx]["name"][0]}_{rank + 1}_{rmsd[batch_idx]:.1f}.pdb') without_rec_overlap_list.append(1 if orig_complex_graph.name[0] in names_no_rec_overlap else 0) names_list.append(orig_complex_graph.name[0]) rmsds_list.append(rmsd) success = 1 except Exception as e: print("Failed on", orig_complex_graph["name"], e) success -= 1 if bs > 1: bs = bs // 2 if success != 1: rmsds_list.append(np.zeros(args.samples_per_complex) + 10000) if confidence_model_args is not None: confidences_list.append(np.zeros(args.samples_per_complex) - 10000) centroid_distances_list.append(np.zeros(args.samples_per_complex) + 10000) min_self_distances_list.append(np.zeros(args.samples_per_complex) + 10000) without_rec_overlap_list.append(1 if orig_complex_graph.name[0] in names_no_rec_overlap else 0) names_list.append(orig_complex_graph.name[0]) failures += 1 print('Performance without hydrogens included in the loss') print(failures, "failures due to exceptions") print(skipped, ' skipped because complex was not in confidence dataset') if args.save_complexes: print("Saving complexes.") if args.complexes_save_path is not None: with open(os.path.join(args.complexes_save_path, "ligands.pkl"), 'wb') as f: pickle.dump(sampled_complexes, f) if args.save_gnina_metrics: with open(f'{args.out_dir}/gnina_metrics.pkl', 'wb') as f: pickle.dump(gnina_metrics, f) print("Saved gnina metrics") performance_metrics = {} for overlap in ['', 'no_overlap_']: if 'no_overlap_' == overlap: without_rec_overlap = np.array(without_rec_overlap_list, dtype=bool) if without_rec_overlap.sum() == 0: continue rmsds = np.array(rmsds_list)[without_rec_overlap] min_self_distances = np.array(min_self_distances_list)[without_rec_overlap] centroid_distances = np.array(centroid_distances_list)[without_rec_overlap] if args.confidence_model_dir is not None: confidences = np.array(confidences_list)[without_rec_overlap] else: confidences = np.array(confidences_list) names = np.array(names_list)[without_rec_overlap] gnina_rmsds = np.array(gnina_rmsds_list)[without_rec_overlap] if args.gnina_minimize else None gnina_score = np.array(gnina_score_list)[without_rec_overlap] if args.gnina_minimize else None else: rmsds = np.array(rmsds_list) gnina_rmsds = np.array(gnina_rmsds_list) if args.gnina_minimize else None gnina_score = np.array(gnina_score_list) if args.gnina_minimize else None min_self_distances = np.array(min_self_distances_list) centroid_distances = np.array(centroid_distances_list) confidences = np.array(confidences_list) names = np.array(names_list) run_times = np.array(run_times) np.save(f'{args.out_dir}/{overlap}min_self_distances.npy', min_self_distances) np.save(f'{args.out_dir}/{overlap}rmsds.npy', rmsds) np.save(f'{args.out_dir}/{overlap}centroid_distances.npy', centroid_distances) np.save(f'{args.out_dir}/{overlap}confidences.npy', confidences) np.save(f'{args.out_dir}/{overlap}run_times.npy', run_times) np.save(f'{args.out_dir}/{overlap}complex_names.npy', np.array(names)) np.save(f'{args.out_dir}/{overlap}gnina_rmsds.npy', gnina_rmsds) np.save(f'{args.out_dir}/{overlap}gnina_score.npy', gnina_score) performance_metrics.update({ f'{overlap}run_times_std': run_times.std().__round__(2), f'{overlap}run_times_mean': run_times.mean().__round__(2), f'{overlap}mean_rmsd': rmsds.mean(), f'{overlap}rmsds_below_2': (100 * (rmsds < 2).sum() / len(rmsds) / N), f'{overlap}rmsds_below_5': (100 * (rmsds < 5).sum() / len(rmsds) / N), f'{overlap}rmsds_percentile_25': np.percentile(rmsds, 25).round(2), f'{overlap}rmsds_percentile_50': np.percentile(rmsds, 50).round(2), f'{overlap}rmsds_percentile_75': np.percentile(rmsds, 75).round(2), f'{overlap}min_rmsds_below_2': (100 * (np.min(rmsds, axis=1) < 2).sum() / len(rmsds)), f'{overlap}min_rmsds_below_5': (100 * (np.min(rmsds, axis=1) < 5).sum() / len(rmsds)), f'{overlap}mean_centroid': centroid_distances.mean().__round__(2), f'{overlap}centroid_below_2': (100 * (centroid_distances < 2).sum() / len(centroid_distances) / N).__round__(2), f'{overlap}centroid_below_5': (100 * (centroid_distances < 5).sum() / len(centroid_distances) / N).__round__(2), f'{overlap}centroid_percentile_25': np.percentile(centroid_distances, 25).round(2), f'{overlap}centroid_percentile_50': np.percentile(centroid_distances, 50).round(2), f'{overlap}centroid_percentile_75': np.percentile(centroid_distances, 75).round(2), }) if args.gnina_minimize: score_ordering = np.argsort(gnina_score, axis=1)[:, ::-1] filtered_rmsds_gnina = gnina_rmsds[np.arange(gnina_rmsds.shape[0])[:, None], score_ordering][:, 0] performance_metrics.update({ f'{overlap}gnina_rmsds_below_2': (100 * (gnina_rmsds < 2).sum() / len(gnina_rmsds) / args.gnina_poses_to_optimize) if args.gnina_minimize else None, f'{overlap}gnina_rmsds_below_5': (100 * (gnina_rmsds < 5).sum() / len(gnina_rmsds) / args.gnina_poses_to_optimize) if args.gnina_minimize else None, f'{overlap}gnina_min_rmsds_below_2': (100 * (np.min(gnina_rmsds, axis=1) < 2).sum() / len(gnina_rmsds)) if args.gnina_minimize else None, f'{overlap}gnina_min_rmsds_below_5': (100 * (np.min(gnina_rmsds, axis=1) < 5).sum() / len(gnina_rmsds)) if args.gnina_minimize else None, f'{overlap}gnina_filtered_rmsds_below_2': (100 * (filtered_rmsds_gnina < 2).sum() / len(filtered_rmsds_gnina)).__round__(2), f'{overlap}gnina_filtered_rmsds_below_5': (100 * (filtered_rmsds_gnina < 5).sum() / len(filtered_rmsds_gnina)).__round__(2), f'{overlap}gnina_rmsds_percentile_25': np.percentile(gnina_rmsds, 25).round(2), f'{overlap}gnina_rmsds_percentile_50': np.percentile(gnina_rmsds, 50).round(2), f'{overlap}gnina_rmsds_percentile_75': np.percentile(gnina_rmsds, 75).round(2), }) if N >= 5: top5_rmsds = np.min(rmsds[:, :5], axis=1) top5_centroid_distances = centroid_distances[ np.arange(rmsds.shape[0])[:, None], np.argsort(rmsds[:, :5], axis=1)][:, 0] top5_min_self_distances = min_self_distances[ np.arange(rmsds.shape[0])[:, None], np.argsort(rmsds[:, :5], axis=1)][:, 0] performance_metrics.update({ f'{overlap}top5_self_intersect_fraction': ( 100 * (top5_min_self_distances < 0.4).sum() / len(top5_min_self_distances)).__round__(2), f'{overlap}top5_rmsds_below_2': (100 * (top5_rmsds < 2).sum() / len(top5_rmsds)).__round__(2), f'{overlap}top5_rmsds_below_5': (100 * (top5_rmsds < 5).sum() / len(top5_rmsds)).__round__(2), f'{overlap}top5_rmsds_percentile_25': np.percentile(top5_rmsds, 25).round(2), f'{overlap}top5_rmsds_percentile_50': np.percentile(top5_rmsds, 50).round(2), f'{overlap}top5_rmsds_percentile_75': np.percentile(top5_rmsds, 75).round(2), f'{overlap}top5_centroid_below_2': ( 100 * (top5_centroid_distances < 2).sum() / len(top5_centroid_distances)).__round__(2), f'{overlap}top5_centroid_below_5': ( 100 * (top5_centroid_distances < 5).sum() / len(top5_centroid_distances)).__round__(2), f'{overlap}top5_centroid_percentile_25': np.percentile(top5_centroid_distances, 25).round(2), f'{overlap}top5_centroid_percentile_50': np.percentile(top5_centroid_distances, 50).round(2), f'{overlap}top5_centroid_percentile_75': np.percentile(top5_centroid_distances, 75).round(2), }) if N >= 10: top10_rmsds = np.min(rmsds[:, :10], axis=1) top10_centroid_distances = centroid_distances[ np.arange(rmsds.shape[0])[:, None], np.argsort(rmsds[:, :10], axis=1)][:, 0] top10_min_self_distances = min_self_distances[ np.arange(rmsds.shape[0])[:, None], np.argsort(rmsds[:, :10], axis=1)][:, 0] performance_metrics.update({ f'{overlap}top10_self_intersect_fraction': ( 100 * (top10_min_self_distances < 0.4).sum() / len(top10_min_self_distances)).__round__(2), f'{overlap}top10_rmsds_below_2': (100 * (top10_rmsds < 2).sum() / len(top10_rmsds)).__round__(2), f'{overlap}top10_rmsds_below_5': (100 * (top10_rmsds < 5).sum() / len(top10_rmsds)).__round__(2), f'{overlap}top10_rmsds_percentile_25': np.percentile(top10_rmsds, 25).round(2), f'{overlap}top10_rmsds_percentile_50': np.percentile(top10_rmsds, 50).round(2), f'{overlap}top10_rmsds_percentile_75': np.percentile(top10_rmsds, 75).round(2), f'{overlap}top10_centroid_below_2': ( 100 * (top10_centroid_distances < 2).sum() / len(top10_centroid_distances)).__round__(2), f'{overlap}top10_centroid_below_5': ( 100 * (top10_centroid_distances < 5).sum() / len(top10_centroid_distances)).__round__(2), f'{overlap}top10_centroid_percentile_25': np.percentile(top10_centroid_distances, 25).round(2), f'{overlap}top10_centroid_percentile_50': np.percentile(top10_centroid_distances, 50).round(2), f'{overlap}top10_centroid_percentile_75': np.percentile(top10_centroid_distances, 75).round(2), }) if confidence_model is not None: confidence_ordering = np.argsort(confidences, axis=1)[:, ::-1] filtered_rmsds = rmsds[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, 0] filtered_centroid_distances = centroid_distances[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, 0] filtered_min_self_distances = min_self_distances[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, 0] performance_metrics.update({ f'{overlap}filtered_self_intersect_fraction': ( 100 * (filtered_min_self_distances < 0.4).sum() / len(filtered_min_self_distances)).__round__( 2), f'{overlap}filtered_rmsds_below_2': (100 * (filtered_rmsds < 2).sum() / len(filtered_rmsds)).__round__(2), f'{overlap}filtered_rmsds_below_5': (100 * (filtered_rmsds < 5).sum() / len(filtered_rmsds)).__round__(2), f'{overlap}filtered_rmsds_percentile_25': np.percentile(filtered_rmsds, 25).round(2), f'{overlap}filtered_rmsds_percentile_50': np.percentile(filtered_rmsds, 50).round(2), f'{overlap}filtered_rmsds_percentile_75': np.percentile(filtered_rmsds, 75).round(2), f'{overlap}filtered_centroid_below_2': ( 100 * (filtered_centroid_distances < 2).sum() / len(filtered_centroid_distances)).__round__(2), f'{overlap}filtered_centroid_below_5': ( 100 * (filtered_centroid_distances < 5).sum() / len(filtered_centroid_distances)).__round__(2), f'{overlap}filtered_centroid_percentile_25': np.percentile(filtered_centroid_distances, 25).round(2), f'{overlap}filtered_centroid_percentile_50': np.percentile(filtered_centroid_distances, 50).round(2), f'{overlap}filtered_centroid_percentile_75': np.percentile(filtered_centroid_distances, 75).round(2), }) if N >= 5: top5_filtered_rmsds = np.min(rmsds[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :5], axis=1) top5_filtered_centroid_distances = \ centroid_distances[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :5][ np.arange(rmsds.shape[0])[:, None], np.argsort( rmsds[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :5], axis=1)][:, 0] top5_filtered_min_self_distances = \ min_self_distances[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :5][ np.arange(rmsds.shape[0])[:, None], np.argsort( rmsds[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :5], axis=1)][:, 0] performance_metrics.update({ f'{overlap}top5_filtered_rmsds_below_2': ( 100 * (top5_filtered_rmsds < 2).sum() / len(top5_filtered_rmsds)).__round__(2), f'{overlap}top5_filtered_rmsds_below_5': ( 100 * (top5_filtered_rmsds < 5).sum() / len(top5_filtered_rmsds)).__round__(2), f'{overlap}top5_filtered_rmsds_percentile_25': np.percentile(top5_filtered_rmsds, 25).round(2), f'{overlap}top5_filtered_rmsds_percentile_50': np.percentile(top5_filtered_rmsds, 50).round(2), f'{overlap}top5_filtered_rmsds_percentile_75': np.percentile(top5_filtered_rmsds, 75).round(2), f'{overlap}top5_filtered_centroid_below_2': (100 * (top5_filtered_centroid_distances < 2).sum() / len( top5_filtered_centroid_distances)).__round__(2), f'{overlap}top5_filtered_centroid_below_5': (100 * (top5_filtered_centroid_distances < 5).sum() / len( top5_filtered_centroid_distances)).__round__(2), f'{overlap}top5_filtered_centroid_percentile_25': np.percentile(top5_filtered_centroid_distances, 25).round(2), f'{overlap}top5_filtered_centroid_percentile_50': np.percentile(top5_filtered_centroid_distances, 50).round(2), f'{overlap}top5_filtered_centroid_percentile_75': np.percentile(top5_filtered_centroid_distances, 75).round(2), }) if N >= 10: top10_filtered_rmsds = np.min(rmsds[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :10], axis=1) top10_filtered_centroid_distances = \ centroid_distances[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :10][ np.arange(rmsds.shape[0])[:, None], np.argsort( rmsds[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :10], axis=1)][:, 0] top10_filtered_min_self_distances = \ min_self_distances[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :10][ np.arange(rmsds.shape[0])[:, None], np.argsort( rmsds[np.arange(rmsds.shape[0])[:, None], confidence_ordering][:, :10], axis=1)][:, 0] performance_metrics.update({ f'{overlap}top10_filtered_rmsds_below_2': ( 100 * (top10_filtered_rmsds < 2).sum() / len(top10_filtered_rmsds)).__round__(2), f'{overlap}top10_filtered_rmsds_below_5': ( 100 * (top10_filtered_rmsds < 5).sum() / len(top10_filtered_rmsds)).__round__(2), f'{overlap}top10_filtered_rmsds_percentile_25': np.percentile(top10_filtered_rmsds, 25).round(2), f'{overlap}top10_filtered_rmsds_percentile_50': np.percentile(top10_filtered_rmsds, 50).round(2), f'{overlap}top10_filtered_rmsds_percentile_75': np.percentile(top10_filtered_rmsds, 75).round(2), f'{overlap}top10_filtered_centroid_below_2': (100 * (top10_filtered_centroid_distances < 2).sum() / len( top10_filtered_centroid_distances)).__round__(2), f'{overlap}top10_filtered_centroid_below_5': (100 * (top10_filtered_centroid_distances < 5).sum() / len( top10_filtered_centroid_distances)).__round__(2), f'{overlap}top10_filtered_centroid_percentile_25': np.percentile(top10_filtered_centroid_distances, 25).round(2), f'{overlap}top10_filtered_centroid_percentile_50': np.percentile(top10_filtered_centroid_distances, 50).round(2), f'{overlap}top10_filtered_centroid_percentile_75': np.percentile(top10_filtered_centroid_distances, 75).round(2), }) for k in performance_metrics: print(k, performance_metrics[k]) if args.wandb: wandb.log(performance_metrics) histogram_metrics_list = [('rmsd', rmsds[:, 0]), ('centroid_distance', centroid_distances[:, 0]), ('mean_rmsd', rmsds.mean(axis=1)), ('mean_centroid_distance', centroid_distances.mean(axis=1))] if N >= 5: histogram_metrics_list.append(('top5_rmsds', top5_rmsds)) histogram_metrics_list.append(('top5_centroid_distances', top5_centroid_distances)) if N >= 10: histogram_metrics_list.append(('top10_rmsds', top10_rmsds)) histogram_metrics_list.append(('top10_centroid_distances', top10_centroid_distances)) if confidence_model is not None: histogram_metrics_list.append(('reverse_filtered_rmsds', reverse_filtered_rmsds)) histogram_metrics_list.append(('reverse_filtered_centroid_distances', reverse_filtered_centroid_distances)) histogram_metrics_list.append(('filtered_rmsd', filtered_rmsds)) histogram_metrics_list.append(('filtered_centroid_distance', filtered_centroid_distances)) if N >= 5: histogram_metrics_list.append(('top5_filtered_rmsds', top5_filtered_rmsds)) histogram_metrics_list.append(('top5_filtered_centroid_distances', top5_filtered_centroid_distances)) histogram_metrics_list.append(('top5_reverse_filtered_rmsds', top5_reverse_filtered_rmsds)) histogram_metrics_list.append( ('top5_reverse_filtered_centroid_distances', top5_reverse_filtered_centroid_distances)) if N >= 10: histogram_metrics_list.append(('top10_filtered_rmsds', top10_filtered_rmsds)) histogram_metrics_list.append(('top10_filtered_centroid_distances', top10_filtered_centroid_distances)) histogram_metrics_list.append(('top10_reverse_filtered_rmsds', top10_reverse_filtered_rmsds)) histogram_metrics_list.append( ('top10_reverse_filtered_centroid_distances', top10_reverse_filtered_centroid_distances))