import copy
import os
import warnings
import numpy as np
import scipy.spatial as spa
import torch
from Bio.PDB import PDBParser
from Bio.PDB.PDBExceptions import PDBConstructionWarning
from rdkit import Chem
from rdkit.Chem.rdchem import BondType as BT
from rdkit.Chem import AllChem, GetPeriodicTable, RemoveHs
from rdkit.Geometry import Point3D
from scipy import spatial
from scipy.special import softmax
from torch_cluster import radius_graph
import torch.nn.functional as F
from datasets.conformer_matching import get_torsion_angles, optimize_rotatable_bonds
from utils.torsion import get_transformation_mask
biopython_parser = PDBParser()
periodic_table = GetPeriodicTable()
allowable_features = {
'possible_atomic_num_list': list(range(1, 119)) + ['misc'],
'possible_chirality_list': [
'CHI_UNSPECIFIED',
'CHI_TETRAHEDRAL_CW',
'CHI_TETRAHEDRAL_CCW',
'CHI_OTHER'
],
'possible_degree_list': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'misc'],
'possible_numring_list': [0, 1, 2, 3, 4, 5, 6, 'misc'],
'possible_implicit_valence_list': [0, 1, 2, 3, 4, 5, 6, 'misc'],
'possible_formal_charge_list': [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 'misc'],
'possible_numH_list': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
'possible_number_radical_e_list': [0, 1, 2, 3, 4, 'misc'],
'possible_hybridization_list': [
'SP', 'SP2', 'SP3', 'SP3D', 'SP3D2', 'misc'
],
'possible_is_aromatic_list': [False, True],
'possible_is_in_ring3_list': [False, True],
'possible_is_in_ring4_list': [False, True],
'possible_is_in_ring5_list': [False, True],
'possible_is_in_ring6_list': [False, True],
'possible_is_in_ring7_list': [False, True],
'possible_is_in_ring8_list': [False, True],
'possible_amino_acids': ['ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET',
'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL', 'HIP', 'HIE', 'TPO', 'HID', 'LEV', 'MEU',
'PTR', 'GLV', 'CYT', 'SEP', 'HIZ', 'CYM', 'GLM', 'ASQ', 'TYS', 'CYX', 'GLZ', 'misc'],
'possible_atom_type_2': ['C*', 'CA', 'CB', 'CD', 'CE', 'CG', 'CH', 'CZ', 'N*', 'ND', 'NE', 'NH', 'NZ', 'O*', 'OD',
'OE', 'OG', 'OH', 'OX', 'S*', 'SD', 'SG', 'misc'],
'possible_atom_type_3': ['C', 'CA', 'CB', 'CD', 'CD1', 'CD2', 'CE', 'CE1', 'CE2', 'CE3', 'CG', 'CG1', 'CG2', 'CH2',
'CZ', 'CZ2', 'CZ3', 'N', 'ND1', 'ND2', 'NE', 'NE1', 'NE2', 'NH1', 'NH2', 'NZ', 'O', 'OD1',
'OD2', 'OE1', 'OE2', 'OG', 'OG1', 'OH', 'OXT', 'SD', 'SG', 'misc'],
}
bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2, BT.AROMATIC: 3}
lig_feature_dims = (list(map(len, [
allowable_features['possible_atomic_num_list'],
allowable_features['possible_chirality_list'],
allowable_features['possible_degree_list'],
allowable_features['possible_formal_charge_list'],
allowable_features['possible_implicit_valence_list'],
allowable_features['possible_numH_list'],
allowable_features['possible_number_radical_e_list'],
allowable_features['possible_hybridization_list'],
allowable_features['possible_is_aromatic_list'],
allowable_features['possible_numring_list'],
allowable_features['possible_is_in_ring3_list'],
allowable_features['possible_is_in_ring4_list'],
allowable_features['possible_is_in_ring5_list'],
allowable_features['possible_is_in_ring6_list'],
allowable_features['possible_is_in_ring7_list'],
allowable_features['possible_is_in_ring8_list'],
])), 0) # number of scalar features
rec_atom_feature_dims = (list(map(len, [
allowable_features['possible_amino_acids'],
allowable_features['possible_atomic_num_list'],
allowable_features['possible_atom_type_2'],
allowable_features['possible_atom_type_3'],
])), 0)
rec_residue_feature_dims = (list(map(len, [
allowable_features['possible_amino_acids']
])), 0)
def lig_atom_featurizer(mol):
ringinfo = mol.GetRingInfo()
atom_features_list = []
for idx, atom in enumerate(mol.GetAtoms()):
atom_features_list.append([
safe_index(allowable_features['possible_atomic_num_list'], atom.GetAtomicNum()),
allowable_features['possible_chirality_list'].index(str(atom.GetChiralTag())),
safe_index(allowable_features['possible_degree_list'], atom.GetTotalDegree()),
safe_index(allowable_features['possible_formal_charge_list'], atom.GetFormalCharge()),
safe_index(allowable_features['possible_implicit_valence_list'], atom.GetImplicitValence()),
safe_index(allowable_features['possible_numH_list'], atom.GetTotalNumHs()),
safe_index(allowable_features['possible_number_radical_e_list'], atom.GetNumRadicalElectrons()),
safe_index(allowable_features['possible_hybridization_list'], str(atom.GetHybridization())),
allowable_features['possible_is_aromatic_list'].index(atom.GetIsAromatic()),
safe_index(allowable_features['possible_numring_list'], ringinfo.NumAtomRings(idx)),
allowable_features['possible_is_in_ring3_list'].index(ringinfo.IsAtomInRingOfSize(idx, 3)),
allowable_features['possible_is_in_ring4_list'].index(ringinfo.IsAtomInRingOfSize(idx, 4)),
allowable_features['possible_is_in_ring5_list'].index(ringinfo.IsAtomInRingOfSize(idx, 5)),
allowable_features['possible_is_in_ring6_list'].index(ringinfo.IsAtomInRingOfSize(idx, 6)),
allowable_features['possible_is_in_ring7_list'].index(ringinfo.IsAtomInRingOfSize(idx, 7)),
allowable_features['possible_is_in_ring8_list'].index(ringinfo.IsAtomInRingOfSize(idx, 8)),
])
return torch.tensor(atom_features_list)
def rec_residue_featurizer(rec):
feature_list = []
for residue in rec.get_residues():
feature_list.append([safe_index(allowable_features['possible_amino_acids'], residue.get_resname())])
return torch.tensor(feature_list, dtype=torch.float32) # (N_res, 1)
def safe_index(l, e):
""" Return index of element e in list l. If e is not present, return the last index """
try:
return l.index(e)
except:
return len(l) - 1
def parse_receptor(pdbid, pdbbind_dir):
rec = parsePDB(pdbid, pdbbind_dir)
return rec
def parsePDB(pdbid, pdbbind_dir):
rec_path = os.path.join(pdbbind_dir, pdbid, f'{pdbid}_protein_processed.pdb')
return parse_pdb_from_path(rec_path)
def parse_pdb_from_path(path):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=PDBConstructionWarning)
structure = biopython_parser.get_structure('random_id', path)
rec = structure[0]
return rec
def extract_receptor_structure(rec, lig, lm_embedding_chains=None):
conf = lig.GetConformer()
lig_coords = conf.GetPositions()
min_distances = []
coords = []
c_alpha_coords = []
n_coords = []
c_coords = []
valid_chain_ids = []
lengths = []
for i, chain in enumerate(rec):
chain_coords = [] # num_residues, num_atoms, 3
chain_c_alpha_coords = []
chain_n_coords = []
chain_c_coords = []
count = 0
invalid_res_ids = []
for res_idx, residue in enumerate(chain):
if residue.get_resname() == 'HOH':
invalid_res_ids.append(residue.get_id())
continue
residue_coords = []
c_alpha, n, c = None, None, None
for atom in residue:
if atom.name == 'CA':
c_alpha = list(atom.get_vector())
if atom.name == 'N':
n = list(atom.get_vector())
if atom.name == 'C':
c = list(atom.get_vector())
residue_coords.append(list(atom.get_vector()))
if c_alpha != None and n != None and c != None:
# only append residue if it is an amino acid and not some weird molecule that is part of the complex
chain_c_alpha_coords.append(c_alpha)
chain_n_coords.append(n)
chain_c_coords.append(c)
chain_coords.append(np.array(residue_coords))
count += 1
else:
invalid_res_ids.append(residue.get_id())
for res_id in invalid_res_ids:
chain.detach_child(res_id)
if len(chain_coords) > 0:
all_chain_coords = np.concatenate(chain_coords, axis=0)
distances = spatial.distance.cdist(lig_coords, all_chain_coords)
min_distance = distances.min()
else:
min_distance = np.inf
min_distances.append(min_distance)
lengths.append(count)
coords.append(chain_coords)
c_alpha_coords.append(np.array(chain_c_alpha_coords))
n_coords.append(np.array(chain_n_coords))
c_coords.append(np.array(chain_c_coords))
if not count == 0: valid_chain_ids.append(chain.get_id())
min_distances = np.array(min_distances)
if len(valid_chain_ids) == 0:
valid_chain_ids.append(np.argmin(min_distances))
valid_coords = []
valid_c_alpha_coords = []
valid_n_coords = []
valid_c_coords = []
valid_lengths = []
invalid_chain_ids = []
valid_lm_embeddings = []
for i, chain in enumerate(rec):
if chain.get_id() in valid_chain_ids:
valid_coords.append(coords[i])
valid_c_alpha_coords.append(c_alpha_coords[i])
if lm_embedding_chains is not None:
if i >= len(lm_embedding_chains):
raise ValueError('Encountered valid chain id that was not present in the LM embeddings')
valid_lm_embeddings.append(lm_embedding_chains[i])
valid_n_coords.append(n_coords[i])
valid_c_coords.append(c_coords[i])
valid_lengths.append(lengths[i])
else:
invalid_chain_ids.append(chain.get_id())
coords = [item for sublist in valid_coords for item in sublist] # list with n_residues arrays: [n_atoms, 3]
c_alpha_coords = np.concatenate(valid_c_alpha_coords, axis=0) # [n_residues, 3]
n_coords = np.concatenate(valid_n_coords, axis=0) # [n_residues, 3]
c_coords = np.concatenate(valid_c_coords, axis=0) # [n_residues, 3]
lm_embeddings = np.concatenate(valid_lm_embeddings, axis=0) if lm_embedding_chains is not None else None
for invalid_id in invalid_chain_ids:
rec.detach_child(invalid_id)
assert len(c_alpha_coords) == len(n_coords)
assert len(c_alpha_coords) == len(c_coords)
assert sum(valid_lengths) == len(c_alpha_coords)
return rec, coords, c_alpha_coords, n_coords, c_coords, lm_embeddings
def get_lig_graph(mol, complex_graph):
lig_coords = torch.from_numpy(mol.GetConformer().GetPositions()).float()
atom_feats = lig_atom_featurizer(mol)
row, col, edge_type = [], [], []
for bond in mol.GetBonds():
start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
row += [start, end]
col += [end, start]
edge_type += 2 * [bonds[bond.GetBondType()]] if bond.GetBondType() != BT.UNSPECIFIED else [0, 0]
edge_index = torch.tensor([row, col], dtype=torch.long)
edge_type = torch.tensor(edge_type, dtype=torch.long)
edge_attr = F.one_hot(edge_type, num_classes=len(bonds)).to(torch.float)
complex_graph['ligand'].x = atom_feats
complex_graph['ligand'].pos = lig_coords
complex_graph['ligand', 'lig_bond', 'ligand'].edge_index = edge_index
complex_graph['ligand', 'lig_bond', 'ligand'].edge_attr = edge_attr
return
def generate_conformer(mol):
ps = AllChem.ETKDGv2()
id = AllChem.EmbedMolecule(mol, ps)
if id == -1:
print('rdkit coords could not be generated without using random coords. using random coords now.')
ps.useRandomCoords = True
AllChem.EmbedMolecule(mol, ps)
AllChem.MMFFOptimizeMolecule(mol, confId=0)
# else:
# AllChem.MMFFOptimizeMolecule(mol_rdkit, confId=0)
def get_lig_graph_with_matching(mol_, complex_graph, popsize, maxiter, matching, keep_original, num_conformers, remove_hs):
if matching:
mol_maybe_noh = copy.deepcopy(mol_)
if remove_hs:
mol_maybe_noh = RemoveHs(mol_maybe_noh, sanitize=True)
if keep_original:
complex_graph['ligand'].orig_pos = mol_maybe_noh.GetConformer().GetPositions()
rotable_bonds = get_torsion_angles(mol_maybe_noh)
if not rotable_bonds: print("no_rotable_bonds but still using it")
for i in range(num_conformers):
mol_rdkit = copy.deepcopy(mol_)
mol_rdkit.RemoveAllConformers()
mol_rdkit = AllChem.AddHs(mol_rdkit)
generate_conformer(mol_rdkit)
if remove_hs:
mol_rdkit = RemoveHs(mol_rdkit, sanitize=True)
mol = copy.deepcopy(mol_maybe_noh)
if rotable_bonds:
optimize_rotatable_bonds(mol_rdkit, mol, rotable_bonds, popsize=popsize, maxiter=maxiter)
mol.AddConformer(mol_rdkit.GetConformer())
rms_list = []
AllChem.AlignMolConformers(mol, RMSlist=rms_list)
mol_rdkit.RemoveAllConformers()
mol_rdkit.AddConformer(mol.GetConformers()[1])
if i == 0:
complex_graph.rmsd_matching = rms_list[0]
get_lig_graph(mol_rdkit, complex_graph)
else:
if torch.is_tensor(complex_graph['ligand'].pos):
complex_graph['ligand'].pos = [complex_graph['ligand'].pos]
complex_graph['ligand'].pos.append(torch.from_numpy(mol_rdkit.GetConformer().GetPositions()).float())
else: # no matching
complex_graph.rmsd_matching = 0
if remove_hs: mol_ = RemoveHs(mol_)
get_lig_graph(mol_, complex_graph)
edge_mask, mask_rotate = get_transformation_mask(complex_graph)
complex_graph['ligand'].edge_mask = torch.tensor(edge_mask)
complex_graph['ligand'].mask_rotate = mask_rotate
return
def get_calpha_graph(rec, c_alpha_coords, n_coords, c_coords, complex_graph, cutoff=20, max_neighbor=None, lm_embeddings=None):
n_rel_pos = n_coords - c_alpha_coords
c_rel_pos = c_coords - c_alpha_coords
num_residues = len(c_alpha_coords)
if num_residues <= 1:
raise ValueError(f"rec contains only 1 residue!")
# Build the k-NN graph
distances = spa.distance.cdist(c_alpha_coords, c_alpha_coords)
src_list = []
dst_list = []
mean_norm_list = []
for i in range(num_residues):
dst = list(np.where(distances[i, :] < cutoff)[0])
dst.remove(i)
if max_neighbor != None and len(dst) > max_neighbor:
dst = list(np.argsort(distances[i, :]))[1: max_neighbor + 1]
if len(dst) == 0:
dst = list(np.argsort(distances[i, :]))[1:2] # choose second because first is i itself
print(f'The c_alpha_cutoff {cutoff} was too small for one c_alpha such that it had no neighbors. '
f'So we connected it to the closest other c_alpha')
assert i not in dst
src = [i] * len(dst)
src_list.extend(src)
dst_list.extend(dst)
valid_dist = list(distances[i, dst])
valid_dist_np = distances[i, dst]
sigma = np.array([1., 2., 5., 10., 30.]).reshape((-1, 1))
weights = softmax(- valid_dist_np.reshape((1, -1)) ** 2 / sigma, axis=1) # (sigma_num, neigh_num)
assert weights[0].sum() > 1 - 1e-2 and weights[0].sum() < 1.01
diff_vecs = c_alpha_coords[src, :] - c_alpha_coords[dst, :] # (neigh_num, 3)
mean_vec = weights.dot(diff_vecs) # (sigma_num, 3)
denominator = weights.dot(np.linalg.norm(diff_vecs, axis=1)) # (sigma_num,)
mean_vec_ratio_norm = np.linalg.norm(mean_vec, axis=1) / denominator # (sigma_num,)
mean_norm_list.append(mean_vec_ratio_norm)
assert len(src_list) == len(dst_list)
node_feat = rec_residue_featurizer(rec)
mu_r_norm = torch.from_numpy(np.array(mean_norm_list).astype(np.float32))
side_chain_vecs = torch.from_numpy(
np.concatenate([np.expand_dims(n_rel_pos, axis=1), np.expand_dims(c_rel_pos, axis=1)], axis=1))
complex_graph['receptor'].x = torch.cat([node_feat, torch.tensor(lm_embeddings)], axis=1) if lm_embeddings is not None else node_feat
complex_graph['receptor'].pos = torch.from_numpy(c_alpha_coords).float()
complex_graph['receptor'].mu_r_norm = mu_r_norm
complex_graph['receptor'].side_chain_vecs = side_chain_vecs.float()
complex_graph['receptor', 'rec_contact', 'receptor'].edge_index = torch.from_numpy(np.asarray([src_list, dst_list]))
return
def rec_atom_featurizer(rec):
atom_feats = []
for i, atom in enumerate(rec.get_atoms()):
atom_name, element = atom.name, atom.element
if element == 'CD':
element = 'C'
assert not element == ''
try:
atomic_num = periodic_table.GetAtomicNumber(element)
except:
atomic_num = -1
atom_feat = [safe_index(allowable_features['possible_amino_acids'], atom.get_parent().get_resname()),
safe_index(allowable_features['possible_atomic_num_list'], atomic_num),
safe_index(allowable_features['possible_atom_type_2'], (atom_name + '*')[:2]),
safe_index(allowable_features['possible_atom_type_3'], atom_name)]
atom_feats.append(atom_feat)
return atom_feats
def get_rec_graph(rec, rec_coords, c_alpha_coords, n_coords, c_coords, complex_graph, rec_radius, c_alpha_max_neighbors=None, all_atoms=False,
atom_radius=5, atom_max_neighbors=None, remove_hs=False, lm_embeddings=None):
if all_atoms:
return get_fullrec_graph(rec, rec_coords, c_alpha_coords, n_coords, c_coords, complex_graph,
c_alpha_cutoff=rec_radius, c_alpha_max_neighbors=c_alpha_max_neighbors,
atom_cutoff=atom_radius, atom_max_neighbors=atom_max_neighbors, remove_hs=remove_hs,lm_embeddings=lm_embeddings)
else:
return get_calpha_graph(rec, c_alpha_coords, n_coords, c_coords, complex_graph, rec_radius, c_alpha_max_neighbors,lm_embeddings=lm_embeddings)
def get_fullrec_graph(rec, rec_coords, c_alpha_coords, n_coords, c_coords, complex_graph, c_alpha_cutoff=20,
c_alpha_max_neighbors=None, atom_cutoff=5, atom_max_neighbors=None, remove_hs=False, lm_embeddings=None):
# builds the receptor graph with both residues and atoms
n_rel_pos = n_coords - c_alpha_coords
c_rel_pos = c_coords - c_alpha_coords
num_residues = len(c_alpha_coords)
if num_residues <= 1:
raise ValueError(f"rec contains only 1 residue!")
# Build the k-NN graph of residues
distances = spa.distance.cdist(c_alpha_coords, c_alpha_coords)
src_list = []
dst_list = []
mean_norm_list = []
for i in range(num_residues):
dst = list(np.where(distances[i, :] < c_alpha_cutoff)[0])
dst.remove(i)
if c_alpha_max_neighbors != None and len(dst) > c_alpha_max_neighbors:
dst = list(np.argsort(distances[i, :]))[1: c_alpha_max_neighbors + 1]
if len(dst) == 0:
dst = list(np.argsort(distances[i, :]))[1:2] # choose second because first is i itself
print(f'The c_alpha_cutoff {c_alpha_cutoff} was too small for one c_alpha such that it had no neighbors. '
f'So we connected it to the closest other c_alpha')
assert i not in dst
src = [i] * len(dst)
src_list.extend(src)
dst_list.extend(dst)
valid_dist = list(distances[i, dst])
valid_dist_np = distances[i, dst]
sigma = np.array([1., 2., 5., 10., 30.]).reshape((-1, 1))
weights = softmax(- valid_dist_np.reshape((1, -1)) ** 2 / sigma, axis=1) # (sigma_num, neigh_num)
assert 1 - 1e-2 < weights[0].sum() < 1.01
diff_vecs = c_alpha_coords[src, :] - c_alpha_coords[dst, :] # (neigh_num, 3)
mean_vec = weights.dot(diff_vecs) # (sigma_num, 3)
denominator = weights.dot(np.linalg.norm(diff_vecs, axis=1)) # (sigma_num,)
mean_vec_ratio_norm = np.linalg.norm(mean_vec, axis=1) / denominator # (sigma_num,)
mean_norm_list.append(mean_vec_ratio_norm)
assert len(src_list) == len(dst_list)
node_feat = rec_residue_featurizer(rec)
mu_r_norm = torch.from_numpy(np.array(mean_norm_list).astype(np.float32))
side_chain_vecs = torch.from_numpy(
np.concatenate([np.expand_dims(n_rel_pos, axis=1), np.expand_dims(c_rel_pos, axis=1)], axis=1))
complex_graph['receptor'].x = torch.cat([node_feat, torch.tensor(lm_embeddings)], axis=1) if lm_embeddings is not None else node_feat
complex_graph['receptor'].pos = torch.from_numpy(c_alpha_coords).float()
complex_graph['receptor'].mu_r_norm = mu_r_norm
complex_graph['receptor'].side_chain_vecs = side_chain_vecs.float()
complex_graph['receptor', 'rec_contact', 'receptor'].edge_index = torch.from_numpy(np.asarray([src_list, dst_list]))
src_c_alpha_idx = np.concatenate([np.asarray([i]*len(l)) for i, l in enumerate(rec_coords)])
atom_feat = torch.from_numpy(np.asarray(rec_atom_featurizer(rec)))
atom_coords = torch.from_numpy(np.concatenate(rec_coords, axis=0)).float()
if remove_hs:
not_hs = (atom_feat[:, 1] != 0)
src_c_alpha_idx = src_c_alpha_idx[not_hs]
atom_feat = atom_feat[not_hs]
atom_coords = atom_coords[not_hs]
atoms_edge_index = radius_graph(atom_coords, atom_cutoff, max_num_neighbors=atom_max_neighbors if atom_max_neighbors else 1000)
atom_res_edge_index = torch.from_numpy(np.asarray([np.arange(len(atom_feat)), src_c_alpha_idx])).long()
complex_graph['atom'].x = atom_feat
complex_graph['atom'].pos = atom_coords
complex_graph['atom', 'atom_contact', 'atom'].edge_index = atoms_edge_index
complex_graph['atom', 'atom_rec_contact', 'receptor'].edge_index = atom_res_edge_index
return
def write_mol_with_coords(mol, new_coords, path):
w = Chem.SDWriter(path)
conf = mol.GetConformer()
for i in range(mol.GetNumAtoms()):
x,y,z = new_coords.astype(np.double)[i]
conf.SetAtomPosition(i,Point3D(x,y,z))
w.write(mol)
w.close()
def read_molecule(molecule_file, sanitize=False, calc_charges=False, remove_hs=False):
if molecule_file.endswith('.mol2'):
mol = Chem.MolFromMol2File(molecule_file, sanitize=False, removeHs=False)
elif molecule_file.endswith('.sdf'):
print(molecule_file)
supplier = Chem.SDMolSupplier(molecule_file, sanitize=False, removeHs=False)
mol = supplier[0]
print(mol)
elif molecule_file.endswith('.pdbqt'):
with open(molecule_file) as file:
pdbqt_data = file.readlines()
pdb_block = ''
for line in pdbqt_data:
pdb_block += '{}\n'.format(line[:66])
mol = Chem.MolFromPDBBlock(pdb_block, sanitize=False, removeHs=False)
elif molecule_file.endswith('.pdb'):
mol = Chem.MolFromPDBFile(molecule_file, sanitize=False, removeHs=False)
else:
return ValueError('Expect the format of the molecule_file to be '
'one of .mol2, .sdf, .pdbqt and .pdb, got {}'.format(molecule_file))
print(sanitize, calc_charges, remove_hs)
try:
if sanitize or calc_charges:
Chem.SanitizeMol(mol)
if calc_charges:
# Compute Gasteiger charges on the molecule.
try:
AllChem.ComputeGasteigerCharges(mol)
except:
warnings.warn('Unable to compute charges for the molecule.')
if remove_hs:
mol = Chem.RemoveHs(mol, sanitize=sanitize)
except Exception as e:
print(e)
return None
return mol
def read_sdf_or_mol2(sdf_fileName, mol2_fileName):
mol = Chem.MolFromMolFile(sdf_fileName, sanitize=False)
problem = False
try:
Chem.SanitizeMol(mol)
mol = Chem.RemoveHs(mol)
except Exception as e:
problem = True
if problem:
mol = Chem.MolFromMol2File(mol2_fileName, sanitize=False)
try:
Chem.SanitizeMol(mol)
mol = Chem.RemoveHs(mol)
problem = False
except Exception as e:
problem = True
return mol, problem