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import binascii
import glob
import hashlib
import os
import pickle
from collections import defaultdict
from multiprocessing import Pool
import random
import copy
import numpy as np
import torch
from rdkit.Chem import MolToSmiles, MolFromSmiles, AddHs
from torch_geometric.data import Dataset, HeteroData
from torch_geometric.loader import DataLoader, DataListLoader
from torch_geometric.transforms import BaseTransform
from tqdm import tqdm
from datasets.process_mols import read_molecule, get_rec_graph, generate_conformer, \
get_lig_graph_with_matching, extract_receptor_structure, parse_receptor, parse_pdb_from_path
from utils.diffusion_utils import modify_conformer, set_time
from utils.utils import read_strings_from_txt
from utils import so3, torus
class NoiseTransform(BaseTransform):
def __init__(self, t_to_sigma, no_torsion, all_atom):
self.t_to_sigma = t_to_sigma
self.no_torsion = no_torsion
self.all_atom = all_atom
def __call__(self, data):
t = np.random.uniform()
t_tr, t_rot, t_tor = t, t, t
return self.apply_noise(data, t_tr, t_rot, t_tor)
def apply_noise(self, data, t_tr, t_rot, t_tor, tr_update = None, rot_update=None, torsion_updates=None):
if not torch.is_tensor(data['ligand'].pos):
data['ligand'].pos = random.choice(data['ligand'].pos)
tr_sigma, rot_sigma, tor_sigma = self.t_to_sigma(t_tr, t_rot, t_tor)
set_time(data, t_tr, t_rot, t_tor, 1, self.all_atom, device=None)
tr_update = torch.normal(mean=0, std=tr_sigma, size=(1, 3)) if tr_update is None else tr_update
rot_update = so3.sample_vec(eps=rot_sigma) if rot_update is None else rot_update
torsion_updates = np.random.normal(loc=0.0, scale=tor_sigma, size=data['ligand'].edge_mask.sum()) if torsion_updates is None else torsion_updates
torsion_updates = None if self.no_torsion else torsion_updates
modify_conformer(data, tr_update, torch.from_numpy(rot_update).float(), torsion_updates)
data.tr_score = -tr_update / tr_sigma ** 2
data.rot_score = torch.from_numpy(so3.score_vec(vec=rot_update, eps=rot_sigma)).float().unsqueeze(0)
data.tor_score = None if self.no_torsion else torch.from_numpy(torus.score(torsion_updates, tor_sigma)).float()
data.tor_sigma_edge = None if self.no_torsion else np.ones(data['ligand'].edge_mask.sum()) * tor_sigma
return data
class PDBBind(Dataset):
def __init__(self, root, transform=None, cache_path='data/cache', split_path='data/', limit_complexes=0,
receptor_radius=30, num_workers=1, c_alpha_max_neighbors=None, popsize=15, maxiter=15,
matching=True, keep_original=False, max_lig_size=None, remove_hs=False, num_conformers=1, all_atoms=False,
atom_radius=5, atom_max_neighbors=None, esm_embeddings_path=None, require_ligand=False,
ligands_list=None, protein_path_list=None, ligand_descriptions=None, keep_local_structures=False):
super(PDBBind, self).__init__(root, transform)
self.pdbbind_dir = root
self.max_lig_size = max_lig_size
self.split_path = split_path
self.limit_complexes = limit_complexes
self.receptor_radius = receptor_radius
self.num_workers = num_workers
self.c_alpha_max_neighbors = c_alpha_max_neighbors
self.remove_hs = remove_hs
self.esm_embeddings_path = esm_embeddings_path
self.require_ligand = require_ligand
self.protein_path_list = protein_path_list
self.ligand_descriptions = ligand_descriptions
self.keep_local_structures = keep_local_structures
if matching or protein_path_list is not None and ligand_descriptions is not None:
cache_path += '_torsion'
if all_atoms:
cache_path += '_allatoms'
self.full_cache_path = os.path.join(cache_path, f'limit{self.limit_complexes}'
f'_INDEX{os.path.splitext(os.path.basename(self.split_path))[0]}'
f'_maxLigSize{self.max_lig_size}_H{int(not self.remove_hs)}'
f'_recRad{self.receptor_radius}_recMax{self.c_alpha_max_neighbors}'
+ ('' if not all_atoms else f'_atomRad{atom_radius}_atomMax{atom_max_neighbors}')
+ ('' if not matching or num_conformers == 1 else f'_confs{num_conformers}')
+ ('' if self.esm_embeddings_path is None else f'_esmEmbeddings')
+ ('' if not keep_local_structures else f'_keptLocalStruct')
+ ('' if protein_path_list is None or ligand_descriptions is None else str(binascii.crc32(''.join(ligand_descriptions + protein_path_list).encode()))))
self.popsize, self.maxiter = popsize, maxiter
self.matching, self.keep_original = matching, keep_original
self.num_conformers = num_conformers
self.all_atoms = all_atoms
self.atom_radius, self.atom_max_neighbors = atom_radius, atom_max_neighbors
if not os.path.exists(os.path.join(self.full_cache_path, "heterographs.pkl"))\
or (require_ligand and not os.path.exists(os.path.join(self.full_cache_path, "rdkit_ligands.pkl"))):
os.makedirs(self.full_cache_path, exist_ok=True)
if protein_path_list is None or ligand_descriptions is None:
self.preprocessing()
else:
self.inference_preprocessing()
print('loading data from memory: ', os.path.join(self.full_cache_path, "heterographs.pkl"))
with open(os.path.join(self.full_cache_path, "heterographs.pkl"), 'rb') as f:
self.complex_graphs = pickle.load(f)
if require_ligand:
with open(os.path.join(self.full_cache_path, "rdkit_ligands.pkl"), 'rb') as f:
self.rdkit_ligands = pickle.load(f)
print_statistics(self.complex_graphs)
def len(self):
return len(self.complex_graphs)
def get(self, idx):
if self.require_ligand:
complex_graph = copy.deepcopy(self.complex_graphs[idx])
complex_graph.mol = copy.deepcopy(self.rdkit_ligands[idx])
return complex_graph
else:
return copy.deepcopy(self.complex_graphs[idx])
def preprocessing(self):
print(f'Processing complexes from [{self.split_path}] and saving it to [{self.full_cache_path}]')
complex_names_all = read_strings_from_txt(self.split_path)
if self.limit_complexes is not None and self.limit_complexes != 0:
complex_names_all = complex_names_all[:self.limit_complexes]
print(f'Loading {len(complex_names_all)} complexes.')
if self.esm_embeddings_path is not None:
id_to_embeddings = torch.load(self.esm_embeddings_path)
chain_embeddings_dictlist = defaultdict(list)
for key, embedding in id_to_embeddings.items():
key_name = key.split('_')[0]
if key_name in complex_names_all:
chain_embeddings_dictlist[key_name].append(embedding)
lm_embeddings_chains_all = []
for name in complex_names_all:
lm_embeddings_chains_all.append(chain_embeddings_dictlist[name])
else:
lm_embeddings_chains_all = [None] * len(complex_names_all)
if self.num_workers > 1:
# running preprocessing in parallel on multiple workers and saving the progress every 1000 complexes
for i in range(len(complex_names_all)//1000+1):
if os.path.exists(os.path.join(self.full_cache_path, f"heterographs{i}.pkl")):
continue
complex_names = complex_names_all[1000*i:1000*(i+1)]
lm_embeddings_chains = lm_embeddings_chains_all[1000*i:1000*(i+1)]
complex_graphs, rdkit_ligands = [], []
if self.num_workers > 1:
p = Pool(self.num_workers, maxtasksperchild=1)
p.__enter__()
with tqdm(total=len(complex_names), desc=f'loading complexes {i}/{len(complex_names_all)//1000+1}') as pbar:
map_fn = p.imap_unordered if self.num_workers > 1 else map
for t in map_fn(self.get_complex, zip(complex_names, lm_embeddings_chains, [None] * len(complex_names), [None] * len(complex_names))):
complex_graphs.extend(t[0])
rdkit_ligands.extend(t[1])
pbar.update()
if self.num_workers > 1: p.__exit__(None, None, None)
with open(os.path.join(self.full_cache_path, f"heterographs{i}.pkl"), 'wb') as f:
pickle.dump((complex_graphs), f)
with open(os.path.join(self.full_cache_path, f"rdkit_ligands{i}.pkl"), 'wb') as f:
pickle.dump((rdkit_ligands), f)
complex_graphs_all = []
for i in range(len(complex_names_all)//1000+1):
with open(os.path.join(self.full_cache_path, f"heterographs{i}.pkl"), 'rb') as f:
l = pickle.load(f)
complex_graphs_all.extend(l)
with open(os.path.join(self.full_cache_path, f"heterographs.pkl"), 'wb') as f:
pickle.dump((complex_graphs_all), f)
rdkit_ligands_all = []
for i in range(len(complex_names_all) // 1000 + 1):
with open(os.path.join(self.full_cache_path, f"rdkit_ligands{i}.pkl"), 'rb') as f:
l = pickle.load(f)
rdkit_ligands_all.extend(l)
with open(os.path.join(self.full_cache_path, f"rdkit_ligands.pkl"), 'wb') as f:
pickle.dump((rdkit_ligands_all), f)
else:
complex_graphs, rdkit_ligands = [], []
with tqdm(total=len(complex_names_all), desc='loading complexes') as pbar:
for t in map(self.get_complex, zip(complex_names_all, lm_embeddings_chains_all, [None] * len(complex_names_all), [None] * len(complex_names_all))):
complex_graphs.extend(t[0])
rdkit_ligands.extend(t[1])
pbar.update()
with open(os.path.join(self.full_cache_path, "heterographs.pkl"), 'wb') as f:
pickle.dump((complex_graphs), f)
with open(os.path.join(self.full_cache_path, "rdkit_ligands.pkl"), 'wb') as f:
pickle.dump((rdkit_ligands), f)
def inference_preprocessing(self):
ligands_list = []
print('Reading molecules and generating local structures with RDKit')
for ligand_description in tqdm(self.ligand_descriptions):
mol = MolFromSmiles(ligand_description) # check if it is a smiles or a path
if mol is not None:
mol = AddHs(mol)
generate_conformer(mol)
ligands_list.append(mol)
else:
mol = read_molecule(ligand_description, remove_hs=False, sanitize=True)
if not self.keep_local_structures:
mol.RemoveAllConformers()
mol = AddHs(mol)
generate_conformer(mol)
ligands_list.append(mol)
if self.esm_embeddings_path is not None:
print('Reading language model embeddings.')
lm_embeddings_chains_all = []
if not os.path.exists(self.esm_embeddings_path): raise Exception('ESM embeddings path does not exist: ',self.esm_embeddings_path)
for protein_path in self.protein_path_list:
embeddings_paths = sorted(glob.glob(os.path.join(self.esm_embeddings_path, os.path.basename(protein_path)) + '*'))
lm_embeddings_chains = []
for embeddings_path in embeddings_paths:
lm_embeddings_chains.append(torch.load(embeddings_path)['representations'][33])
lm_embeddings_chains_all.append(lm_embeddings_chains)
else:
lm_embeddings_chains_all = [None] * len(self.protein_path_list)
print('Generating graphs for ligands and proteins')
if self.num_workers > 1:
# running preprocessing in parallel on multiple workers and saving the progress every 1000 complexes
for i in range(len(self.protein_path_list)//1000+1):
if os.path.exists(os.path.join(self.full_cache_path, f"heterographs{i}.pkl")):
continue
protein_paths_chunk = self.protein_path_list[1000*i:1000*(i+1)]
ligand_description_chunk = self.ligand_descriptions[1000*i:1000*(i+1)]
ligands_chunk = ligands_list[1000 * i:1000 * (i + 1)]
lm_embeddings_chains = lm_embeddings_chains_all[1000*i:1000*(i+1)]
complex_graphs, rdkit_ligands = [], []
if self.num_workers > 1:
p = Pool(self.num_workers, maxtasksperchild=1)
p.__enter__()
with tqdm(total=len(protein_paths_chunk), desc=f'loading complexes {i}/{len(protein_paths_chunk)//1000+1}') as pbar:
map_fn = p.imap_unordered if self.num_workers > 1 else map
for t in map_fn(self.get_complex, zip(protein_paths_chunk, lm_embeddings_chains, ligands_chunk,ligand_description_chunk)):
complex_graphs.extend(t[0])
rdkit_ligands.extend(t[1])
pbar.update()
if self.num_workers > 1: p.__exit__(None, None, None)
with open(os.path.join(self.full_cache_path, f"heterographs{i}.pkl"), 'wb') as f:
pickle.dump((complex_graphs), f)
with open(os.path.join(self.full_cache_path, f"rdkit_ligands{i}.pkl"), 'wb') as f:
pickle.dump((rdkit_ligands), f)
complex_graphs_all = []
for i in range(len(self.protein_path_list)//1000+1):
with open(os.path.join(self.full_cache_path, f"heterographs{i}.pkl"), 'rb') as f:
l = pickle.load(f)
complex_graphs_all.extend(l)
with open(os.path.join(self.full_cache_path, f"heterographs.pkl"), 'wb') as f:
pickle.dump((complex_graphs_all), f)
rdkit_ligands_all = []
for i in range(len(self.protein_path_list) // 1000 + 1):
with open(os.path.join(self.full_cache_path, f"rdkit_ligands{i}.pkl"), 'rb') as f:
l = pickle.load(f)
rdkit_ligands_all.extend(l)
with open(os.path.join(self.full_cache_path, f"rdkit_ligands.pkl"), 'wb') as f:
pickle.dump((rdkit_ligands_all), f)
else:
complex_graphs, rdkit_ligands = [], []
with tqdm(total=len(self.protein_path_list), desc='loading complexes') as pbar:
for t in map(self.get_complex, zip(self.protein_path_list, lm_embeddings_chains_all, ligands_list, self.ligand_descriptions)):
complex_graphs.extend(t[0])
rdkit_ligands.extend(t[1])
pbar.update()
with open(os.path.join(self.full_cache_path, "heterographs.pkl"), 'wb') as f:
pickle.dump((complex_graphs), f)
with open(os.path.join(self.full_cache_path, "rdkit_ligands.pkl"), 'wb') as f:
pickle.dump((rdkit_ligands), f)
def get_complex(self, par):
name, lm_embedding_chains, ligand, ligand_description = par
if not os.path.exists(os.path.join(self.pdbbind_dir, name)) and ligand is None:
print("Folder not found", name)
return [], []
if ligand is not None:
rec_model = parse_pdb_from_path(name)
name = f'{name}____{ligand_description}'
ligs = [ligand]
else:
try:
rec_model = parse_receptor(name, self.pdbbind_dir)
except Exception as e:
print(f'Skipping {name} because of the error:')
print(e)
return [], []
ligs = read_mols(self.pdbbind_dir, name, remove_hs=False)
complex_graphs = []
for i, lig in enumerate(ligs):
if self.max_lig_size is not None and lig.GetNumHeavyAtoms() > self.max_lig_size:
print(f'Ligand with {lig.GetNumHeavyAtoms()} heavy atoms is larger than max_lig_size {self.max_lig_size}. Not including {name} in preprocessed data.')
continue
complex_graph = HeteroData()
complex_graph['name'] = name
try:
get_lig_graph_with_matching(lig, complex_graph, self.popsize, self.maxiter, self.matching, self.keep_original,
self.num_conformers, remove_hs=self.remove_hs)
rec, rec_coords, c_alpha_coords, n_coords, c_coords, lm_embeddings = extract_receptor_structure(copy.deepcopy(rec_model), lig, lm_embedding_chains=lm_embedding_chains)
if lm_embeddings is not None and len(c_alpha_coords) != len(lm_embeddings):
print(f'LM embeddings for complex {name} did not have the right length for the protein. Skipping {name}.')
continue
get_rec_graph(rec, rec_coords, c_alpha_coords, n_coords, c_coords, complex_graph, rec_radius=self.receptor_radius,
c_alpha_max_neighbors=self.c_alpha_max_neighbors, all_atoms=self.all_atoms,
atom_radius=self.atom_radius, atom_max_neighbors=self.atom_max_neighbors, remove_hs=self.remove_hs, lm_embeddings=lm_embeddings)
except Exception as e:
print(f'Skipping {name} because of the error:')
print(e)
raise e
continue
protein_center = torch.mean(complex_graph['receptor'].pos, dim=0, keepdim=True)
complex_graph['receptor'].pos -= protein_center
if self.all_atoms:
complex_graph['atom'].pos -= protein_center
if (not self.matching) or self.num_conformers == 1:
complex_graph['ligand'].pos -= protein_center
else:
for p in complex_graph['ligand'].pos:
p -= protein_center
complex_graph.original_center = protein_center
complex_graphs.append(complex_graph)
return complex_graphs, ligs
def print_statistics(complex_graphs):
statistics = ([], [], [], [])
for complex_graph in complex_graphs:
lig_pos = complex_graph['ligand'].pos if torch.is_tensor(complex_graph['ligand'].pos) else complex_graph['ligand'].pos[0]
radius_protein = torch.max(torch.linalg.vector_norm(complex_graph['receptor'].pos, dim=1))
molecule_center = torch.mean(lig_pos, dim=0)
radius_molecule = torch.max(
torch.linalg.vector_norm(lig_pos - molecule_center.unsqueeze(0), dim=1))
distance_center = torch.linalg.vector_norm(molecule_center)
statistics[0].append(radius_protein)
statistics[1].append(radius_molecule)
statistics[2].append(distance_center)
if "rmsd_matching" in complex_graph:
statistics[3].append(complex_graph.rmsd_matching)
else:
statistics[3].append(0)
name = ['radius protein', 'radius molecule', 'distance protein-mol', 'rmsd matching']
print('Number of complexes: ', len(complex_graphs))
for i in range(4):
array = np.asarray(statistics[i])
print(f"{name[i]}: mean {np.mean(array)}, std {np.std(array)}, max {np.max(array)}")
def construct_loader(args, t_to_sigma):
transform = NoiseTransform(t_to_sigma=t_to_sigma, no_torsion=args.no_torsion,
all_atom=args.all_atoms)
common_args = {'transform': transform, 'root': args.data_dir, 'limit_complexes': args.limit_complexes,
'receptor_radius': args.receptor_radius,
'c_alpha_max_neighbors': args.c_alpha_max_neighbors,
'remove_hs': args.remove_hs, 'max_lig_size': args.max_lig_size,
'matching': not args.no_torsion, 'popsize': args.matching_popsize, 'maxiter': args.matching_maxiter,
'num_workers': args.num_workers, 'all_atoms': args.all_atoms,
'atom_radius': args.atom_radius, 'atom_max_neighbors': args.atom_max_neighbors,
'esm_embeddings_path': args.esm_embeddings_path}
train_dataset = PDBBind(cache_path=args.cache_path, split_path=args.split_train, keep_original=True,
num_conformers=args.num_conformers, **common_args)
val_dataset = PDBBind(cache_path=args.cache_path, split_path=args.split_val, keep_original=True, **common_args)
loader_class = DataListLoader if torch.cuda.is_available() else DataLoader
train_loader = loader_class(dataset=train_dataset, batch_size=args.batch_size, num_workers=args.num_dataloader_workers, shuffle=True, pin_memory=args.pin_memory)
val_loader = loader_class(dataset=val_dataset, batch_size=args.batch_size, num_workers=args.num_dataloader_workers, shuffle=True, pin_memory=args.pin_memory)
return train_loader, val_loader
def read_mol(pdbbind_dir, name, remove_hs=False):
lig = read_molecule(os.path.join(pdbbind_dir, name, f'{name}_ligand.sdf'), remove_hs=remove_hs, sanitize=True)
if lig is None: # read mol2 file if sdf file cannot be sanitized
lig = read_molecule(os.path.join(pdbbind_dir, name, f'{name}_ligand.mol2'), remove_hs=remove_hs, sanitize=True)
return lig
def read_mols(pdbbind_dir, name, remove_hs=False):
ligs = []
for file in os.listdir(os.path.join(pdbbind_dir, name)):
if file.endswith(".sdf") and 'rdkit' not in file:
lig = read_molecule(os.path.join(pdbbind_dir, name, file), remove_hs=remove_hs, sanitize=True)
if lig is None and os.path.exists(os.path.join(pdbbind_dir, name, file[:-4] + ".mol2")): # read mol2 file if sdf file cannot be sanitized
print('Using the .sdf file failed. We found a .mol2 file instead and are trying to use that.')
lig = read_molecule(os.path.join(pdbbind_dir, name, file[:-4] + ".mol2"), remove_hs=remove_hs, sanitize=True)
if lig is not None:
ligs.append(lig)
return ligs |