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import copy |
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import os |
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import torch |
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import time |
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from argparse import ArgumentParser, Namespace, FileType |
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from datetime import datetime |
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from functools import partial |
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import numpy as np |
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import wandb |
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from biopandas.pdb import PandasPdb |
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from rdkit import RDLogger |
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from torch_geometric.loader import DataLoader |
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from datasets.pdbbind import PDBBind, read_mol |
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from utils.diffusion_utils import t_to_sigma as t_to_sigma_compl, get_t_schedule |
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from utils.sampling import randomize_position, sampling |
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from utils.utils import get_model, get_symmetry_rmsd, remove_all_hs, read_strings_from_txt, ExponentialMovingAverage |
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from utils.visualise import PDBFile |
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from tqdm import tqdm |
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RDLogger.DisableLog('rdApp.*') |
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import yaml |
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cache_name = datetime.now().strftime('date%d-%m_time%H-%M-%S.%f') |
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parser = ArgumentParser() |
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parser.add_argument('--config', type=FileType(mode='r'), default=None) |
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parser.add_argument('--model_dir', type=str, default='workdir', help='Path to folder with trained score model and hyperparameters') |
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parser.add_argument('--ckpt', type=str, default='best_model.pt', help='Checkpoint to use inside the folder') |
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parser.add_argument('--confidence_model_dir', type=str, default=None, help='Path to folder with trained confidence model and hyperparameters') |
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parser.add_argument('--confidence_ckpt', type=str, default='best_model.pt', help='Checkpoint to use inside the folder') |
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parser.add_argument('--affinity_model_dir', type=str, default=None, help='Path to folder with trained affinity model and hyperparameters') |
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parser.add_argument('--affinity_ckpt', type=str, default='best_model.pt', help='Checkpoint to use inside the folder') |
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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.') |
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parser.add_argument('--run_name', type=str, default='test', help='') |
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parser.add_argument('--project', type=str, default='ligbind_inf', help='') |
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parser.add_argument('--out_dir', type=str, default=None, help='Where to save results to') |
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parser.add_argument('--batch_size', type=int, default=10, help='Number of poses to sample in parallel') |
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parser.add_argument('--cache_path', type=str, default='data/cacheNew', help='Folder from where to load/restore cached dataset') |
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parser.add_argument('--data_dir', type=str, default='data/PDBBind_processed/', help='Folder containing original structures') |
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parser.add_argument('--split_path', type=str, default='data/splits/timesplit_no_lig_overlap_val', help='Path of file defining the split') |
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parser.add_argument('--no_model', action='store_true', default=False, help='Whether to return seed conformer without running model') |
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parser.add_argument('--no_random', action='store_true', default=False, help='Whether to add randomness in diffusion steps') |
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parser.add_argument('--no_final_step_noise', action='store_true', default=False, help='Whether to add noise after the final step') |
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parser.add_argument('--ode', action='store_true', default=False, help='Whether to run the probability flow ODE') |
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parser.add_argument('--wandb', action='store_true', default=False, help='') |
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parser.add_argument('--inference_steps', type=int, default=20, help='Number of denoising steps') |
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parser.add_argument('--limit_complexes', type=int, default=0, help='Limit to the number of complexes') |
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parser.add_argument('--num_workers', type=int, default=1, help='Number of workers for dataset creation') |
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parser.add_argument('--tqdm', action='store_true', default=False, help='Whether to show progress bar') |
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parser.add_argument('--save_visualisation', action='store_true', default=False, help='Whether to save visualizations') |
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parser.add_argument('--samples_per_complex', type=int, default=1, help='Number of poses to sample for each complex') |
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parser.add_argument('--actual_steps', type=int, default=None, help='') |
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args = parser.parse_args() |
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if args.config: |
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config_dict = yaml.load(args.config, Loader=yaml.FullLoader) |
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arg_dict = args.__dict__ |
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for key, value in config_dict.items(): |
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if isinstance(value, list): |
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for v in value: |
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arg_dict[key].append(v) |
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else: |
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arg_dict[key] = value |
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if args.out_dir is None: args.out_dir = f'inference_out_dir_not_specified/{args.run_name}' |
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os.makedirs(args.out_dir, exist_ok=True) |
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with open(f'{args.model_dir}/model_parameters.yml') as f: |
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score_model_args = Namespace(**yaml.full_load(f)) |
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if args.confidence_model_dir is not None: |
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with open(f'{args.confidence_model_dir}/model_parameters.yml') as f: |
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confidence_args = Namespace(**yaml.full_load(f)) |
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if not os.path.exists(confidence_args.original_model_dir): |
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print("Path does not exist: ", confidence_args.original_model_dir) |
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confidence_args.original_model_dir = os.path.join(*confidence_args.original_model_dir.split('/')[-2:]) |
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print('instead trying path: ', confidence_args.original_model_dir) |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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test_dataset = PDBBind(transform=None, root=args.data_dir, limit_complexes=args.limit_complexes, |
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receptor_radius=score_model_args.receptor_radius, |
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cache_path=args.cache_path, split_path=args.split_path, |
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remove_hs=score_model_args.remove_hs, max_lig_size=None, |
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c_alpha_max_neighbors=score_model_args.c_alpha_max_neighbors, |
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matching=not score_model_args.no_torsion, keep_original=True, |
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popsize=score_model_args.matching_popsize, |
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maxiter=score_model_args.matching_maxiter, |
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all_atoms=score_model_args.all_atoms, |
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atom_radius=score_model_args.atom_radius, |
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atom_max_neighbors=score_model_args.atom_max_neighbors, |
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esm_embeddings_path=score_model_args.esm_embeddings_path, |
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require_ligand=True, |
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num_workers=args.num_workers) |
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test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False) |
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if args.confidence_model_dir is not None: |
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if not (confidence_args.use_original_model_cache or confidence_args.transfer_weights): |
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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.') |
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confidence_test_dataset = PDBBind(transform=None, root=args.data_dir, limit_complexes=args.limit_complexes, |
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receptor_radius=confidence_args.receptor_radius, |
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cache_path=args.cache_path, split_path=args.split_path, |
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remove_hs=confidence_args.remove_hs, max_lig_size=None, c_alpha_max_neighbors=confidence_args.c_alpha_max_neighbors, |
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matching=not confidence_args.no_torsion, keep_original=True, |
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popsize=confidence_args.matching_popsize, |
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maxiter=confidence_args.matching_maxiter, |
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all_atoms=confidence_args.all_atoms, |
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atom_radius=confidence_args.atom_radius, |
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atom_max_neighbors=confidence_args.atom_max_neighbors, |
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esm_embeddings_path= confidence_args.esm_embeddings_path, require_ligand=True, |
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num_workers=args.num_workers) |
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confidence_complex_dict = {d.name: d for d in confidence_test_dataset} |
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t_to_sigma = partial(t_to_sigma_compl, args=score_model_args) |
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if not args.no_model: |
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model = get_model(score_model_args, device, t_to_sigma=t_to_sigma, no_parallel=True) |
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state_dict = torch.load(f'{args.model_dir}/{args.ckpt}', map_location=torch.device('cpu')) |
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if args.ckpt == 'last_model.pt': |
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model_state_dict = state_dict['model'] |
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ema_weights_state = state_dict['ema_weights'] |
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model.load_state_dict(model_state_dict, strict=True) |
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ema_weights = ExponentialMovingAverage(model.parameters(), decay=score_model_args.ema_rate) |
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ema_weights.load_state_dict(ema_weights_state, device=device) |
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ema_weights.copy_to(model.parameters()) |
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else: |
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model.load_state_dict(state_dict, strict=True) |
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model = model.to(device) |
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model.eval() |
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if args.confidence_model_dir is not None: |
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if confidence_args.transfer_weights: |
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with open(f'{confidence_args.original_model_dir}/model_parameters.yml') as f: |
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confidence_model_args = Namespace(**yaml.full_load(f)) |
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else: |
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confidence_model_args = confidence_args |
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confidence_model = get_model(confidence_model_args, device, t_to_sigma=t_to_sigma, no_parallel=True, |
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confidence_mode=True) |
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state_dict = torch.load(f'{args.confidence_model_dir}/{args.confidence_ckpt}', map_location=torch.device('cpu')) |
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confidence_model.load_state_dict(state_dict, strict=True) |
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confidence_model = confidence_model.to(device) |
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confidence_model.eval() |
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else: |
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confidence_model = None |
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confidence_args = None |
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confidence_model_args = None |
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if args.wandb: |
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run = wandb.init( |
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entity='entity', |
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settings=wandb.Settings(start_method="fork"), |
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project=args.project, |
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name=args.run_name, |
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config=args |
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) |
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tr_schedule = get_t_schedule(inference_steps=args.inference_steps) |
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rot_schedule = tr_schedule |
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tor_schedule = tr_schedule |
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print('t schedule', tr_schedule) |
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rmsds_list, obrmsds, centroid_distances_list, failures, skipped, min_cross_distances_list, base_min_cross_distances_list, confidences_list, names_list = [], [], [], 0, 0, [], [], [], [] |
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true_affinities_list, pred_affinities_list, run_times, min_self_distances_list, without_rec_overlap_list = [], [], [], [], [] |
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N = args.samples_per_complex |
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names_no_rec_overlap = read_strings_from_txt(f'data/splits/timesplit_test_no_rec_overlap') |
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print('Size of test dataset: ', len(test_dataset)) |
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for idx, orig_complex_graph in tqdm(enumerate(test_loader)): |
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if confidence_model is not None and not (confidence_args.use_original_model_cache or |
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confidence_args.transfer_weights) and orig_complex_graph.name[0] not in confidence_complex_dict.keys(): |
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skipped += 1 |
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print(f"HAPPENING | The confidence dataset did not contain {orig_complex_graph.name[0]}. We are skipping this complex.") |
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continue |
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success = 0 |
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while not success: |
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try: |
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success = 1 |
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data_list = [copy.deepcopy(orig_complex_graph) for _ in range(N)] |
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randomize_position(data_list, score_model_args.no_torsion, args.no_random, score_model_args.tr_sigma_max) |
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pdb = None |
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if args.save_visualisation: |
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visualization_list = [] |
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for idx, graph in enumerate(data_list): |
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lig = read_mol(args.data_dir, graph['name'][0], remove_hs=score_model_args.remove_hs) |
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pdb = PDBFile(lig) |
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pdb.add(lig, 0, 0) |
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pdb.add((orig_complex_graph['ligand'].pos + orig_complex_graph.original_center).detach().cpu(), 1, 0) |
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pdb.add((graph['ligand'].pos + graph.original_center).detach().cpu(), part=1, order=1) |
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visualization_list.append(pdb) |
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else: |
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visualization_list = None |
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rec_path = os.path.join(args.data_dir, data_list[0]["name"][0], f'{data_list[0]["name"][0]}_protein_processed.pdb') |
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if not os.path.exists(rec_path): |
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rec_path = os.path.join(args.data_dir, data_list[0]["name"][0], f'{data_list[0]["name"][0]}_protein_obabel_reduce.pdb') |
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rec = PandasPdb().read_pdb(rec_path) |
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rec_df = rec.df['ATOM'] |
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receptor_pos = rec_df[['x_coord', 'y_coord', 'z_coord']].to_numpy().squeeze().astype( |
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np.float32) - orig_complex_graph.original_center.cpu().numpy() |
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receptor_pos = np.tile(receptor_pos, (N, 1, 1)) |
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start_time = time.time() |
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if not args.no_model: |
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if confidence_model is not None and not ( |
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confidence_args.use_original_model_cache or confidence_args.transfer_weights): |
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confidence_data_list = [copy.deepcopy(confidence_complex_dict[orig_complex_graph.name[0]]) for _ in |
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range(N)] |
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else: |
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confidence_data_list = None |
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data_list, confidence = sampling(data_list=data_list, model=model, |
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inference_steps=args.actual_steps if args.actual_steps is not None else args.inference_steps, |
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tr_schedule=tr_schedule, rot_schedule=rot_schedule, |
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tor_schedule=tor_schedule, |
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device=device, t_to_sigma=t_to_sigma, model_args=score_model_args, |
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no_random=args.no_random, |
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ode=args.ode, visualization_list=visualization_list, |
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confidence_model=confidence_model, |
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confidence_data_list=confidence_data_list, |
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confidence_model_args=confidence_model_args, |
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batch_size=args.batch_size, |
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no_final_step_noise=args.no_final_step_noise) |
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run_times.append(time.time() - start_time) |
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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()) |
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filterHs = torch.not_equal(data_list[0]['ligand'].x[:, 0], 0).cpu().numpy() |
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if isinstance(orig_complex_graph['ligand'].orig_pos, list): |
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orig_complex_graph['ligand'].orig_pos = orig_complex_graph['ligand'].orig_pos[0] |
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ligand_pos = np.asarray( |
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[complex_graph['ligand'].pos.cpu().numpy()[filterHs] for complex_graph in data_list]) |
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orig_ligand_pos = np.expand_dims( |
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orig_complex_graph['ligand'].orig_pos[filterHs] - orig_complex_graph.original_center.cpu().numpy(), |
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axis=0) |
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try: |
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mol = remove_all_hs(orig_complex_graph.mol[0]) |
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rmsd = get_symmetry_rmsd(mol, orig_ligand_pos[0], [l for l in ligand_pos]) |
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except Exception as e: |
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print("Using non corrected RMSD because of the error", e) |
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rmsd = np.sqrt(((ligand_pos - orig_ligand_pos) ** 2).sum(axis=2).mean(axis=1)) |
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rmsds_list.append(rmsd) |
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centroid_distance = np.linalg.norm(ligand_pos.mean(axis=1) - orig_ligand_pos.mean(axis=1), axis=1) |
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if confidence is not None and isinstance(confidence_args.rmsd_classification_cutoff, list): |
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confidence = confidence[:, 0] |
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if confidence is not None: |
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confidence = confidence.cpu().numpy() |
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re_order = np.argsort(confidence)[::-1] |
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print(orig_complex_graph['name'], ' rmsd', np.around(rmsd, 1)[re_order], ' centroid distance', |
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np.around(centroid_distance, 1)[re_order], ' confidences ', np.around(confidence, 4)[re_order]) |
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confidences_list.append(confidence) |
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else: |
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print(orig_complex_graph['name'], ' rmsd', np.around(rmsd, 1), ' centroid distance', |
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np.around(centroid_distance, 1)) |
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centroid_distances_list.append(centroid_distance) |
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cross_distances = np.linalg.norm(receptor_pos[:, :, None, :] - ligand_pos[:, None, :, :], axis=-1) |
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min_cross_distances_list.append(np.min(cross_distances, axis=(1, 2))) |
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self_distances = np.linalg.norm(ligand_pos[:, :, None, :] - ligand_pos[:, None, :, :], axis=-1) |
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self_distances = np.where(np.eye(self_distances.shape[2]), np.inf, self_distances) |
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min_self_distances_list.append(np.min(self_distances, axis=(1, 2))) |
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base_cross_distances = np.linalg.norm(receptor_pos[:, :, None, :] - orig_ligand_pos[:, None, :, :], axis=-1) |
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base_min_cross_distances_list.append(np.min(base_cross_distances, axis=(1, 2))) |
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if args.save_visualisation: |
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if confidence is not None: |
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for rank, batch_idx in enumerate(re_order): |
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visualization_list[batch_idx].write( |
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f'{args.out_dir}/{data_list[batch_idx]["name"][0]}_{rank + 1}_{rmsd[batch_idx]:.1f}_{(confidence)[batch_idx]:.1f}.pdb') |
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else: |
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for rank, batch_idx in enumerate(np.argsort(rmsd)): |
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visualization_list[batch_idx].write( |
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f'{args.out_dir}/{data_list[batch_idx]["name"][0]}_{rank + 1}_{rmsd[batch_idx]:.1f}.pdb') |
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without_rec_overlap_list.append(1 if orig_complex_graph.name[0] in names_no_rec_overlap else 0) |
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names_list.append(orig_complex_graph.name[0]) |
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except Exception as e: |
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print("Failed on", orig_complex_graph["name"], e) |
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failures += 1 |
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success = 0 |
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print('Performance without hydrogens included in the loss') |
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print(failures, "failures due to exceptions") |
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print(skipped, ' skipped because complex was not in confidence dataset') |
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performance_metrics = {} |
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for overlap in ['', 'no_overlap_']: |
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if 'no_overlap_' == overlap: |
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without_rec_overlap = np.array(without_rec_overlap_list, dtype=bool) |
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if without_rec_overlap.sum() == 0: continue |
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rmsds = np.array(rmsds_list)[without_rec_overlap] |
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min_self_distances = np.array(min_self_distances_list)[without_rec_overlap] |
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centroid_distances = np.array(centroid_distances_list)[without_rec_overlap] |
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confidences = np.array(confidences_list)[without_rec_overlap] |
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min_cross_distances = np.array(min_cross_distances_list)[without_rec_overlap] |
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base_min_cross_distances = np.array(base_min_cross_distances_list)[without_rec_overlap] |
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names = np.array(names_list)[without_rec_overlap] |
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else: |
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rmsds = np.array(rmsds_list) |
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min_self_distances = np.array(min_self_distances_list) |
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centroid_distances = np.array(centroid_distances_list) |
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confidences = np.array(confidences_list) |
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min_cross_distances = np.array(min_cross_distances_list) |
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base_min_cross_distances = np.array(base_min_cross_distances_list) |
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names = np.array(names_list) |
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run_times = np.array(run_times) |
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np.save(f'{args.out_dir}/{overlap}min_cross_distances.npy', min_cross_distances) |
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np.save(f'{args.out_dir}/{overlap}min_self_distances.npy', min_self_distances) |
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np.save(f'{args.out_dir}/{overlap}base_min_cross_distances.npy', base_min_cross_distances) |
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np.save(f'{args.out_dir}/{overlap}rmsds.npy', rmsds) |
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np.save(f'{args.out_dir}/{overlap}centroid_distances.npy', centroid_distances) |
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np.save(f'{args.out_dir}/{overlap}confidences.npy', confidences) |
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np.save(f'{args.out_dir}/{overlap}run_times.npy', run_times) |
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np.save(f'{args.out_dir}/{overlap}complex_names.npy', np.array(names)) |
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|
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performance_metrics.update({ |
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f'{overlap}run_times_std': run_times.std().__round__(2), |
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f'{overlap}run_times_mean': run_times.mean().__round__(2), |
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f'{overlap}steric_clash_fraction': ( |
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100 * (min_cross_distances < 0.4).sum() / len(min_cross_distances) / N).__round__(2), |
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f'{overlap}self_intersect_fraction': ( |
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100 * (min_self_distances < 0.4).sum() / len(min_self_distances) / N).__round__(2), |
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f'{overlap}mean_rmsd': rmsds.mean(), |
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f'{overlap}rmsds_below_2': (100 * (rmsds < 2).sum() / len(rmsds) / N), |
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f'{overlap}rmsds_below_5': (100 * (rmsds < 5).sum() / len(rmsds) / N), |
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f'{overlap}rmsds_percentile_25': np.percentile(rmsds, 25).round(2), |
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f'{overlap}rmsds_percentile_50': np.percentile(rmsds, 50).round(2), |
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f'{overlap}rmsds_percentile_75': np.percentile(rmsds, 75).round(2), |
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f'{overlap}mean_centroid': centroid_distances.mean().__round__(2), |
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f'{overlap}centroid_below_2': (100 * (centroid_distances < 2).sum() / len(centroid_distances) / N).__round__(2), |
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f'{overlap}centroid_below_5': (100 * (centroid_distances < 5).sum() / len(centroid_distances) / N).__round__(2), |
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f'{overlap}centroid_percentile_25': np.percentile(centroid_distances, 25).round(2), |
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f'{overlap}centroid_percentile_50': np.percentile(centroid_distances, 50).round(2), |
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f'{overlap}centroid_percentile_75': np.percentile(centroid_distances, 75).round(2), |
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}) |
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|
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if N >= 5: |
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top5_rmsds = np.min(rmsds[:, :5], axis=1) |
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top5_centroid_distances = centroid_distances[ |
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np.arange(rmsds.shape[0])[:, None], np.argsort(rmsds[:, :5], axis=1)][:, 0] |
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top5_min_cross_distances = min_cross_distances[ |
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np.arange(rmsds.shape[0])[:, None], np.argsort(rmsds[:, :5], axis=1)][:, 0] |
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top5_min_self_distances = min_self_distances[ |
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np.arange(rmsds.shape[0])[:, None], np.argsort(rmsds[:, :5], axis=1)][:, 0] |
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performance_metrics.update({ |
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f'{overlap}top5_steric_clash_fraction': ( |
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100 * (top5_min_cross_distances < 0.4).sum() / len(top5_min_cross_distances)).__round__(2), |
|
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_cross_distances = min_cross_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_steric_clash_fraction': ( |
|
100 * (top10_min_cross_distances < 0.4).sum() / len(top10_min_cross_distances)).__round__(2), |
|
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_cross_distances = min_cross_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_steric_clash_fraction': ( |
|
100 * (filtered_min_cross_distances < 0.4).sum() / len(filtered_min_cross_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_cross_distances = \ |
|
min_cross_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_self_intersect_fraction': ( |
|
100 * (top5_filtered_min_cross_distances < 0.4).sum() / len( |
|
top5_filtered_min_cross_distances)).__round__(2), |
|
f'{overlap}top5_filtered_steric_clash_fraction': ( |
|
100 * (top5_filtered_min_cross_distances < 0.4).sum() / len( |
|
top5_filtered_min_cross_distances)).__round__(2), |
|
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_cross_distances = \ |
|
min_cross_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_self_intersect_fraction': ( |
|
100 * (top10_filtered_min_cross_distances < 0.4).sum() / len( |
|
top10_filtered_min_cross_distances)).__round__(2), |
|
f'{overlap}top10_filtered_steric_clash_fraction': ( |
|
100 * (top10_filtered_min_cross_distances < 0.4).sum() / len( |
|
top10_filtered_min_cross_distances)).__round__(2), |
|
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(('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)) |
|
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)) |
|
|
