import os import shutil import argparse import random import torch import numpy as np import math import subprocess import multiprocessing as mp from functools import partial from torch_geometric.data import Batch from tqdm.auto import tqdm from rdkit import Chem from rdkit.Geometry import Point3D from torch.utils.data import DataLoader from rdkit.Chem.rdchem import BondType from rdkit.Chem import ChemicalFeatures, rdMolDescriptors from rdkit import RDConfig from rdkit.Chem.Descriptors import MolLogP, qed from copy import deepcopy import tempfile import contextlib from torch_scatter import scatter_add, scatter_mean from rdkit.Geometry import Point3D from models.flag import FLAG from utils.transforms import * from utils.datasets import get_dataset from utils.misc import * from utils.data import * from utils.mol_tree import * from utils.chemutils import * from utils.dihedral_utils import * from utils.sascorer import compute_sa_score from rdkit.Chem import AllChem _fscores = None ATOM_FAMILIES = ['Acceptor', 'Donor', 'Aromatic', 'Hydrophobe', 'LumpedHydrophobe', 'NegIonizable', 'PosIonizable', 'ZnBinder'] ATOM_FAMILIES_ID = {s: i for i, s in enumerate(ATOM_FAMILIES)} STATUS_RUNNING = 'running' STATUS_FINISHED = 'finished' STATUS_FAILED = 'failed' def supress_stdout(func): def wrapper(*a, **ka): with open(os.devnull, 'w') as devnull: with contextlib.redirect_stdout(devnull): return func(*a, **ka) return wrapper def get_feat(mol): fdefName = os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef') factory = ChemicalFeatures.BuildFeatureFactory(fdefName) atomic_numbers = torch.LongTensor([6, 7, 8, 9, 15, 16, 17]) # C N O F P S Cl ptable = Chem.GetPeriodicTable() Chem.SanitizeMol(mol) feat_mat = np.zeros([mol.GetNumAtoms(), len(ATOM_FAMILIES)], dtype=np.int_) for feat in factory.GetFeaturesForMol(mol): feat_mat[feat.GetAtomIds(), ATOM_FAMILIES_ID[feat.GetFamily()]] = 1 ligand_element = torch.tensor([ptable.GetAtomicNumber(atom.GetSymbol()) for atom in mol.GetAtoms()]) element = ligand_element.view(-1, 1) == atomic_numbers.view(1, -1) # (N_atoms, N_elements) return torch.cat([element, torch.tensor(feat_mat)], dim=-1).float() def find_reference(protein_pos, focal_id): # Select three reference protein atoms d = torch.norm(protein_pos - protein_pos[focal_id], dim=1) reference_idx = torch.topk(d, k=4, largest=False)[1] reference_pos = protein_pos[reference_idx] return reference_pos, reference_idx def SetAtomNum(mol, atoms): for atom in mol.GetAtoms(): if atom.GetIdx() in atoms: atom.SetAtomMapNum(1) else: atom.SetAtomMapNum(0) return mol def SetMolPos(mol_list, pos_list): new_mol_list = [] for i in range(len(pos_list)): mol = mol_list[i] conf = mol.GetConformer(0) pos = pos_list[i].cpu().double().numpy() if mol.GetNumAtoms() == len(pos): for node in range(mol.GetNumAtoms()): x, y, z = pos[node] conf.SetAtomPosition(node, Point3D(x,y,z)) try: AllChem.UFFOptimizeMolecule(mol) new_mol_list.append(mol) except: new_mol_list.append(mol) return new_mol_list def lipinski(mol): count = 0 if qed(mol) <= 5: count += 1 if Chem.Lipinski.NumHDonors(mol) <= 5: count += 1 if Chem.Lipinski.NumHAcceptors(mol) <= 10: count += 1 if Chem.Descriptors.ExactMolWt(mol) <= 500: count += 1 if Chem.Lipinski.NumRotatableBonds(mol) <= 5: count += 1 return count def refine_pos(ligand_pos, protein_pos, h_ctx_ligand, h_ctx_protein, model, batch, repeats, protein_batch, ligand_batch): protein_offsets = torch.cumsum(protein_batch.bincount(), dim=0) ligand_offsets = torch.cumsum(ligand_batch.bincount(), dim=0) protein_offsets, ligand_offsets = torch.cat([torch.tensor([0]), protein_offsets]), torch.cat([torch.tensor([0]), ligand_offsets]) sr_ligand_idx, sr_protein_idx = [], [] sr_ligand_idx0, sr_ligand_idx1 = [], [] for i in range(len(repeats)): alpha_index = batch['alpha_carbon_indicator'][protein_batch == i].nonzero().reshape(-1) ligand_atom_index = torch.arange(repeats[i]) p_idx, q_idx = torch.cartesian_prod(ligand_atom_index, torch.arange(len(alpha_index))).chunk(2, dim=-1) p_idx, q_idx = p_idx.squeeze(-1), q_idx.squeeze(-1) sr_ligand_idx.append(ligand_atom_index[p_idx] + ligand_offsets[i]) sr_protein_idx.append(alpha_index[q_idx] + protein_offsets[i]) p_idx, q_idx = torch.cartesian_prod(ligand_atom_index, ligand_atom_index).chunk(2, dim=-1) p_idx, q_idx = p_idx.squeeze(-1), q_idx.squeeze(-1) sr_ligand_idx0.append(ligand_atom_index[p_idx] + ligand_offsets[i]) sr_ligand_idx1.append(ligand_atom_index[q_idx] + ligand_offsets[i]) sr_ligand_idx, sr_protein_idx = torch.cat(sr_ligand_idx).long(), torch.cat(sr_protein_idx).long() sr_ligand_idx0, sr_ligand_idx1 = torch.cat(sr_ligand_idx0).long(), torch.cat(sr_ligand_idx1).long() dist_alpha = torch.norm(ligand_pos[sr_ligand_idx] - protein_pos[sr_protein_idx], dim=1) dist_intra = torch.norm(ligand_pos[sr_ligand_idx0] - ligand_pos[sr_ligand_idx1], dim=1) input_dir_alpha = ligand_pos[sr_ligand_idx] - protein_pos[sr_protein_idx] input_dir_intra = ligand_pos[sr_ligand_idx0] - ligand_pos[sr_ligand_idx1] distance_emb1 = model.encoder.distance_expansion(torch.norm(input_dir_alpha, dim=1)) distance_emb2 = model.encoder.distance_expansion(torch.norm(input_dir_intra, dim=1)) input1 = torch.cat([h_ctx_ligand[sr_ligand_idx], h_ctx_protein[sr_protein_idx], distance_emb1], dim=-1)[dist_alpha <= 10.0] input2 = torch.cat([h_ctx_ligand[sr_ligand_idx0], h_ctx_ligand[sr_ligand_idx1], distance_emb2], dim=-1)[dist_intra <= 10.0] # distance cut_off norm_dir1 = F.normalize(input_dir_alpha, p=2, dim=1)[dist_alpha <= 10.0] norm_dir2 = F.normalize(input_dir_intra, p=2, dim=1)[dist_intra <= 10.0] force1 = scatter_mean(model.refine_protein(input1) * norm_dir1, dim=0, index=sr_ligand_idx[dist_alpha <= 10.0], dim_size=ligand_pos.size(0)) force2 = scatter_mean(model.refine_ligand(input2) * norm_dir2, dim=0, index=sr_ligand_idx0[dist_intra <= 10.0], dim_size=ligand_pos.size(0)) ligand_pos += force1 ligand_pos += force2 ligand_pos = [ligand_pos[ligand_batch==k].float() for k in range(len(repeats))] return ligand_pos def ligand_gen(batch, model, vocab, config, center, device, refinement=False): pos_list = [] feat_list = [] motif_id = [0 for _ in range(config.sample.batch_size)] finished = torch.zeros(config.sample.batch_size).bool() for i in range(config.sample.max_steps): print(i) print(finished) if torch.sum(finished) == config.sample.batch_size: # mol_list = SetMolPos(mol_list, pos_list) return mol_list, pos_list if i == 0: focal_pred, mask_protein, h_ctx = model(protein_pos=batch['protein_pos'], protein_atom_feature=batch['protein_atom_feature'].float(), ligand_pos=batch['ligand_context_pos'], ligand_atom_feature=batch['ligand_context_feature_full'].float(), batch_protein=batch['protein_element_batch'], batch_ligand=batch['ligand_context_element_batch']) protein_atom_feature = batch['protein_atom_feature'].float() focal_protein = focal_pred[mask_protein] h_ctx_protein = h_ctx[mask_protein] focus_score = torch.sigmoid(focal_protein) #can_focus = focus_score > 0.5 slice_idx = torch.cat([torch.tensor([0]).to(device), torch.cumsum(batch['protein_element_batch'].bincount(), dim=0)]) focal_id = [] for j in range(len(slice_idx) - 1): focus = focus_score[slice_idx[j]:slice_idx[j + 1]] focal_id.append(torch.argmax(focus.reshape(-1).float()).item() + slice_idx[j].item()) focal_id = torch.tensor(focal_id, device=device) h_ctx_focal = h_ctx_protein[focal_id] current_wid = torch.tensor([vocab.size()] * config.sample.batch_size, device=device) next_motif_wid, motif_prob = model.forward_motif(h_ctx_focal, current_wid, torch.arange(config.sample.batch_size, device=device).to(device)) mol_list = [Chem.MolFromSmiles(vocab.get_smiles(id)) for id in next_motif_wid] for j in range(config.sample.batch_size): AllChem.EmbedMolecule(mol_list[j]) AllChem.UFFOptimizeMolecule(mol_list[j]) ligand_pos, ligand_feat = torch.tensor(mol_list[j].GetConformer().GetPositions(), device=device), get_feat(mol_list[j]).to(device) feat_list.append(ligand_feat) # set the initial positions with distance matrix reference_pos, reference_idx = find_reference(batch['protein_pos'][slice_idx[j]:slice_idx[j + 1]], focal_id[j] - slice_idx[j]) p_idx, l_idx = torch.cartesian_prod(torch.arange(4), torch.arange(len(ligand_pos))).chunk(2, dim=-1) p_idx = p_idx.squeeze(-1).to(device) l_idx = l_idx.squeeze(-1).to(device) d_m = model.dist_mlp(torch.cat([protein_atom_feature[reference_idx[p_idx]], ligand_feat[l_idx]], dim=-1)).reshape(4,len(ligand_pos)) d_m = d_m ** 2 p_d, l_d = self_square_dist(reference_pos), self_square_dist(ligand_pos) D = torch.cat([torch.cat([p_d, d_m], dim=1), torch.cat([d_m.permute(1, 0), l_d], dim=1)]) coordinate = eig_coord_from_dist(D) new_pos, _, _ = kabsch_torch(coordinate[:len(reference_pos)], reference_pos, coordinate[len(reference_pos):]) # new_pos += (center*0.8+torch.mean(reference_pos, dim=0)*0.2) - torch.mean(new_pos, dim=0) new_pos += (center - torch.mean(new_pos, dim=0)) * .8 pos_list.append(new_pos) atom_to_motif = [{} for _ in range(config.sample.batch_size)] motif_to_atoms = [{} for _ in range(config.sample.batch_size)] motif_wid = [{} for _ in range(config.sample.batch_size)] for j in range(config.sample.batch_size): for k in range(mol_list[j].GetNumAtoms()): atom_to_motif[j][k] = 0 for j in range(config.sample.batch_size): motif_to_atoms[j][0] = list(np.arange(mol_list[j].GetNumAtoms())) motif_wid[j][0] = next_motif_wid[j].item() else: repeats = torch.tensor([len(pos) for pos in pos_list], device=device) ligand_batch = torch.repeat_interleave(torch.arange(config.sample.batch_size, device=device), repeats) focal_pred, mask_protein, h_ctx = model(protein_pos=batch['protein_pos'].float(), protein_atom_feature=batch['protein_atom_feature'].float(), ligand_pos=torch.cat(pos_list, dim=0).float(), ligand_atom_feature=torch.cat(feat_list, dim=0).float(), batch_protein=batch['protein_element_batch'], batch_ligand=ligand_batch) # structure refinement if refinement: pos_list = refine_pos(torch.cat(pos_list, dim=0).float(), batch['protein_pos'].float(), h_ctx[~mask_protein], h_ctx[mask_protein], model, batch, repeats.tolist(), batch['protein_element_batch'], ligand_batch) focal_ligand = focal_pred[~mask_protein] h_ctx_ligand = h_ctx[~mask_protein] focus_score = torch.sigmoid(focal_ligand) can_focus = focus_score > 0. slice_idx = torch.cat([torch.tensor([0], device=device), torch.cumsum(repeats, dim=0)]) current_atoms_batch, current_atoms = [], [] for j in range(len(slice_idx) - 1): focus = focus_score[slice_idx[j]:slice_idx[j + 1]] if torch.sum(can_focus[slice_idx[j]:slice_idx[j + 1]]) > 0 and ~finished[j]: sample_focal_atom = torch.multinomial(focus.reshape(-1).float(), 1) focal_motif = atom_to_motif[j][sample_focal_atom.item()] motif_id[j] = focal_motif else: finished[j] = True current_atoms.extend((np.array(motif_to_atoms[j][motif_id[j]]) + slice_idx[j].item()).tolist()) current_atoms_batch.extend([j] * len(motif_to_atoms[j][motif_id[j]])) mol_list[j] = SetAtomNum(mol_list[j], motif_to_atoms[j][motif_id[j]]) # second step: next motif prediction current_wid = [motif_wid[j][motif_id[j]] for j in range(len(mol_list))] next_motif_wid, motif_prob = model.forward_motif(h_ctx_ligand[torch.tensor(current_atoms)], torch.tensor(current_wid).to(device), torch.tensor(current_atoms_batch).to(device)) # assemble next_motif_smiles = [vocab.get_smiles(id) for id in next_motif_wid] new_mol_list, new_atoms, one_atom_attach, intersection, attach_fail = model.forward_attach(mol_list, next_motif_smiles, device) for j in range(len(mol_list)): if ~finished[j] and ~attach_fail[j]: # num_new_atoms mol_list[j] = new_mol_list[j] rotatable = torch.logical_and(torch.tensor(current_atoms_batch).bincount() == 2, torch.tensor(one_atom_attach)) rotatable = torch.logical_and(rotatable, ~torch.tensor(attach_fail)) rotatable = torch.logical_and(rotatable, ~finished).to(device) # update motif2atoms and atom2motif for j in range(len(mol_list)): if attach_fail[j] or finished[j]: continue motif_to_atoms[j][i] = new_atoms[j] motif_wid[j][i] = next_motif_wid[j] for k in new_atoms[j]: atom_to_motif[j][k] = i ''' if k in atom_to_motif[j]: continue else: atom_to_motif[j][k] = i''' # generate initial positions for j in range(len(mol_list)): if attach_fail[j] or finished[j]: continue mol = mol_list[j] anchor = [atom.GetIdx() for atom in mol.GetAtoms() if atom.GetAtomMapNum() == 1] # positions = mol.GetConformer().GetPositions() anchor_pos = deepcopy(pos_list[j][anchor]).to(device) Chem.SanitizeMol(mol) AllChem.EmbedMolecule(mol, useRandomCoords=True) try: AllChem.UFFOptimizeMolecule(mol) except: print('UFF error') anchor_pos_new = mol.GetConformer(0).GetPositions()[anchor] new_idx = [atom.GetIdx() for atom in mol.GetAtoms() if atom.GetAtomMapNum() == 2] ''' R, T = kabsch(np.matrix(anchor_pos), np.matrix(anchor_pos_new)) new_pos = R * np.matrix(mol.GetConformer().GetPositions()[new_idx]).T + np.tile(T, (1, len(new_idx))) new_pos = np.array(new_pos.T)''' new_pos = mol.GetConformer().GetPositions()[new_idx] new_pos, _, _ = kabsch_torch(torch.tensor(anchor_pos_new, device=device), anchor_pos, torch.tensor(new_pos, device=device)) conf = mol.GetConformer() # update curated parameters pos_list[j] = torch.cat([pos_list[j], new_pos]) feat_list[j] = get_feat(mol_list[j]).to(device) for node in range(mol.GetNumAtoms()): conf.SetAtomPosition(node, np.array(pos_list[j][node].cpu())) assert mol.GetNumAtoms() == len(pos_list[j]) # predict alpha and rotate (only change the position) if torch.sum(rotatable) > 0 and i >= 2: repeats = torch.tensor([len(pos) for pos in pos_list]) ligand_batch = torch.repeat_interleave(torch.arange(len(pos_list)), repeats).to(device) slice_idx = torch.cat([torch.tensor([0]), torch.cumsum(repeats, dim=0)]) xy_index = [(np.array(motif_to_atoms[j][motif_id[j]]) + slice_idx[j].item()).tolist() for j in range(len(slice_idx) - 1) if rotatable[j]] alpha = model.forward_alpha(protein_pos=batch['protein_pos'].float(), protein_atom_feature=batch['protein_atom_feature'].float(), ligand_pos=torch.cat(pos_list, dim=0).float(), ligand_atom_feature=torch.cat(feat_list, dim=0).float(), batch_protein=batch['protein_element_batch'], batch_ligand=ligand_batch, xy_index=torch.tensor(xy_index, device=device), rotatable=rotatable) rotatable_id = [id for id in range(len(mol_list)) if rotatable[id]] xy_index = [motif_to_atoms[j][motif_id[j]] for j in range(len(slice_idx) - 1) if rotatable[j]] x_index = [intersection[j] for j in range(len(slice_idx) - 1) if rotatable[j]] y_index = [(set(xy_index[k]) - set(x_index[k])).pop() for k in range(len(x_index))] for j in range(len(alpha)): mol = mol_list[rotatable_id[j]] new_idx = [atom.GetIdx() for atom in mol.GetAtoms() if atom.GetAtomMapNum() == 2] positions = deepcopy(pos_list[rotatable_id[j]]) xn_pos = positions[new_idx].float() dir=(positions[x_index[j]] - positions[y_index[j]]).reshape(-1) ref=positions[x_index[j]].reshape(-1) xn_pos = rand_rotate(dir.to(device), ref.to(device), xn_pos.to(device), alpha[j], device=device) if xn_pos.shape[0] > 0: pos_list[rotatable_id[j]][-len(xn_pos):] = xn_pos conf = mol.GetConformer() for node in range(mol.GetNumAtoms()): conf.SetAtomPosition(node, np.array(pos_list[rotatable_id[j]][node].cpu())) assert mol.GetNumAtoms() == len(pos_list[rotatable_id[j]]) return mol_list, pos_list def demo(data_id): vocab_path = 'vocab.txt' device = 'cpu' config = './configs/sample.yml' vocab = [] for line in open(vocab_path): p, _, _ = line.partition(':') vocab.append(p) vocab = Vocab(vocab) # Load configs config = load_config(config) # Data protein_featurizer = FeaturizeProteinAtom() ligand_featurizer = FeaturizeLigandAtom() masking = LigandMaskAll(vocab) transform = Compose([ LigandCountNeighbors(), protein_featurizer, ligand_featurizer, FeaturizeLigandBond(), masking, ]) dataset, subsets = get_dataset( config=config.dataset, transform=transform, ) testset = subsets['test'] data = testset[data_id%100] center = data['ligand_center'].to(device) test_set = [data for _ in range(config.sample.num_samples)] # Model (Main) ckpt = torch.load(config.model.checkpoint, map_location=device) model = FLAG( ckpt['config'].model, protein_atom_feature_dim=protein_featurizer.feature_dim, ligand_atom_feature_dim=ligand_featurizer.feature_dim, vocab=vocab, ).to(device) model.load_state_dict(ckpt['model']) # my code goes here sample_loader = DataLoader(test_set, batch_size=config.sample.batch_size, shuffle=False, num_workers=config.sample.num_workers, collate_fn=collate_mols) with torch.no_grad(): model.eval() number = 0 for batch in tqdm(sample_loader): for key in batch: batch[key] = batch[key].to(device) gen_data, pos_list = ligand_gen(batch, model, vocab, config, center, device) SetMolPos(gen_data, pos_list) for mol in gen_data: try: AllChem.UFFOptimizeMolecule(mol) except: print('UFF error') for _, mol in enumerate(gen_data): number += 1 if mol.GetNumAtoms() < 12 or MolLogP(mol) < 0.60: continue filename = os.path.join('./data', 'Ligand.sdf') writer = Chem.SDWriter(filename) # writer.SetKekulize(False) writer.write(mol, confId=0) writer.close() return filename if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--config', type=str, default='./configs/sample.yml') parser.add_argument('-i', '--data_id', type=int, default=0) parser.add_argument('--device', type=str, default='cuda:0') parser.add_argument('--outdir', type=str, default='./outputs') parser.add_argument('--vocab_path', type=str, default='vocab.txt') parser.add_argument('--num_workers', type=int, default=64) args = parser.parse_args() # Load vocab vocab = [] for line in open(args.vocab_path): p, _, _ = line.partition(':') vocab.append(p) vocab = Vocab(vocab) # Load configs config = load_config(args.config) config_name = os.path.basename(args.config)[:os.path.basename(args.config).rfind('.')] seed_all(config.sample.seed) # Logging log_dir = get_new_log_dir(args.outdir, prefix='%s-%d' % (config_name, args.data_id)) logger = get_logger('sample', log_dir) logger.info(args) logger.info(config) shutil.copyfile(args.config, os.path.join(log_dir, os.path.basename(args.config))) # Data logger.info('Loading data...') protein_featurizer = FeaturizeProteinAtom() ligand_featurizer = FeaturizeLigandAtom() masking = LigandMaskAll(vocab) transform = Compose([ LigandCountNeighbors(), protein_featurizer, ligand_featurizer, FeaturizeLigandBond(), masking, ]) dataset, subsets = get_dataset( config=config.dataset, transform=transform, ) testset = subsets['test'] data = testset[args.data_id] center = data['ligand_center'].to(args.device) test_set = [data for _ in range(config.sample.num_samples)] with open(os.path.join(log_dir, 'pocket_info.txt'), 'a') as f: f.write(data['protein_filename'] + '\n') # Model (Main) logger.info('Loading main model...') ckpt = torch.load(config.model.checkpoint, map_location=args.device) model = FLAG( ckpt['config'].model, protein_atom_feature_dim=protein_featurizer.feature_dim, ligand_atom_feature_dim=ligand_featurizer.feature_dim, vocab=vocab, ).to(args.device) model.load_state_dict(ckpt['model']) # my code goes here sample_loader = DataLoader(test_set, batch_size=config.sample.batch_size, shuffle=False, num_workers=config.sample.num_workers, collate_fn=collate_mols) data_list = [] try: with torch.no_grad(): model.eval() number = 0 number_list = [] for batch in tqdm(sample_loader): for key in batch: batch[key] = batch[key].to(args.device) gen_data, pos_list = ligand_gen(batch, model, vocab, config, center, args.device) SetMolPos(gen_data, pos_list) for mol in gen_data: try: AllChem.UFFOptimizeMolecule(mol) except: print('UFF error') data_list.extend(gen_data) with open(os.path.join(log_dir, 'SMILES.txt'), 'a') as smiles_f: for _, mol in enumerate(gen_data): number+=1 if mol.GetNumAtoms() < 12 or MolLogP(mol) < 0.60: continue smiles_f.write(Chem.MolToSmiles(mol) + '\n') writer = Chem.SDWriter(os.path.join(log_dir, '%d.sdf' % number)) # writer.SetKekulize(False) writer.write(mol, confId=0) writer.close() number_list.append(number) # Calculate metrics print([Chem.MolToSmiles(mol) for mol in data_list]) smiles = [Chem.MolFromSmiles(Chem.MolToSmiles(mol)) for mol in data_list] qed_list = [qed(mol) for mol in smiles if mol.GetNumAtoms() >= 8] logp_list = [MolLogP(mol) for mol in smiles] sa_list = [compute_sa_score(mol) for mol in smiles] Lip_list = [lipinski(mol) for mol in smiles] print('QED %.6f | LogP %.6f | SA %.6f | Lipinski %.6f \n' % (np.average(qed_list), np.average(logp_list), np.average(sa_list), np.average(Lip_list))) except KeyboardInterrupt: logger.info('Terminated. Generated molecules will be saved.') with open(os.path.join(log_dir, 'SMILES.txt'), 'a') as smiles_f: for i, mol in enumerate(data_list): if mol.GetNumAtoms() < 12 or MolLogP(mol) < 0.60: continue smiles_f.write(Chem.MolToSmiles(mol) + '\n') writer = Chem.SDWriter(os.path.join(log_dir, '%d.sdf' % i)) # writer.SetKekulize(False) writer.write(mol, confId=0) writer.close() pool = mp.Pool(args.num_workers) vina_list = [] pro_path = '/n/holyscratch01/mzitnik_lab/zaixizhang/pdbbind_pocket10/' + os.path.join(data['pdbid'], data['pdbid']+'_pocket.pdb') for vina_score in tqdm(pool.imap_unordered(partial(calculate_vina, pro_path=pro_path, lig_path=log_dir), number_list), total=len(number_list)): if vina_score != None: vina_list.append(vina_score) pool.close() print('Vina: ', np.average(vina_list))