-
Notifications
You must be signed in to change notification settings - Fork 71
/
optimize.py
249 lines (191 loc) · 9.33 KB
/
optimize.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
import argparse
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from Bio.PDB import PDBParser
from rdkit import Chem
import pandas as pd
import random
from torch_scatter import scatter_mean
from openbabel import openbabel
openbabel.obErrorLog.StopLogging() # suppress OpenBabel messages
import utils
from lightning_modules import LigandPocketDDPM
from constants import FLOAT_TYPE, INT_TYPE
from analysis.molecule_builder import build_molecule, process_molecule
from analysis.metrics import MoleculeProperties
def prepare_from_sdf_files(sdf_files, atom_encoder):
ligand_coords = []
atom_one_hot = []
for file in sdf_files:
rdmol = Chem.SDMolSupplier(str(file), sanitize=False)[0]
ligand_coords.append(
torch.from_numpy(rdmol.GetConformer().GetPositions()).float()
)
types = torch.tensor([atom_encoder[a.GetSymbol()] for a in rdmol.GetAtoms()])
atom_one_hot.append(
F.one_hot(types, num_classes=len(atom_encoder))
)
return torch.cat(ligand_coords, dim=0), torch.cat(atom_one_hot, dim=0)
def prepare_ligands_from_mols(mols, atom_encoder, device='cpu'):
ligand_coords = []
atom_one_hots = []
masks = []
sizes = []
for i, mol in enumerate(mols):
coord = torch.tensor(mol.GetConformer().GetPositions(), dtype=FLOAT_TYPE)
types = torch.tensor([atom_encoder[a.GetSymbol()] for a in mol.GetAtoms()], dtype=INT_TYPE)
one_hot = F.one_hot(types, num_classes=len(atom_encoder))
mask = torch.ones(len(types), dtype=INT_TYPE) * i
ligand_coords.append(coord)
atom_one_hots.append(one_hot)
masks.append(mask)
sizes.append(len(types))
ligand = {
'x': torch.cat(ligand_coords, dim=0).to(device),
'one_hot': torch.cat(atom_one_hots, dim=0).to(device),
'size': torch.tensor(sizes, dtype=INT_TYPE).to(device),
'mask': torch.cat(masks, dim=0).to(device),
}
return ligand
def prepare_ligand_from_pdb(biopython_atoms, atom_encoder):
coord = torch.tensor(np.array([a.get_coord()
for a in biopython_atoms]), dtype=FLOAT_TYPE)
types = torch.tensor([atom_encoder[a.element.capitalize()]
for a in biopython_atoms])
one_hot = F.one_hot(types, num_classes=len(atom_encoder))
return coord, one_hot
def prepare_substructure(ref_ligand, fix_atoms, pdb_model):
if fix_atoms[0].endswith(".sdf"):
# ligand as sdf file
coord, one_hot = prepare_from_sdf_files(fix_atoms, model.lig_type_encoder)
else:
# ligand contained in PDB; given in <chain>:<resi> format
chain, resi = ref_ligand.split(':')
ligand = utils.get_residue_with_resi(pdb_model[chain], int(resi))
fixed_atoms = [a for a in ligand.get_atoms() if a.get_name() in set(fix_atoms)]
coord, one_hot = prepare_ligand_from_pdb(fixed_atoms, model.lig_type_encoder)
return coord, one_hot
def diversify_ligands(model, pocket, mols, timesteps,
sanitize=False,
largest_frag=False,
relax_iter=0):
"""
Diversify ligands for a specified pocket.
Parameters:
model: The model instance used for diversification.
pocket: The pocket information including coordinates and types.
mols: List of RDKit molecule objects to be diversified.
timesteps: Number of denoising steps to apply during diversification.
sanitize: If True, performs molecule sanitization post-generation (default: False).
largest_frag: If True, only the largest fragment of the generated molecule is returned (default: False).
relax_iter: Number of iterations for force field relaxation of the generated molecules (default: 0).
Returns:
A list of diversified RDKit molecule objects.
"""
ligand = prepare_ligands_from_mols(mols, model.lig_type_encoder, device=model.device)
pocket_mask = pocket['mask']
lig_mask = ligand['mask']
# Pocket's center of mass
pocket_com_before = scatter_mean(pocket['x'], pocket['mask'], dim=0)
out_lig, out_pocket, _, _ = model.ddpm.diversify(ligand, pocket, noising_steps=timesteps)
# Move generated molecule back to the original pocket position
pocket_com_after = scatter_mean(out_pocket[:, :model.x_dims], pocket_mask, dim=0)
out_pocket[:, :model.x_dims] += \
(pocket_com_before - pocket_com_after)[pocket_mask]
out_lig[:, :model.x_dims] += \
(pocket_com_before - pocket_com_after)[lig_mask]
# Build mol objects
x = out_lig[:, :model.x_dims].detach().cpu()
atom_type = out_lig[:, model.x_dims:].argmax(1).detach().cpu()
molecules = []
for mol_pc in zip(utils.batch_to_list(x, lig_mask),
utils.batch_to_list(atom_type, lig_mask)):
mol = build_molecule(*mol_pc, model.dataset_info, add_coords=True)
mol = process_molecule(mol,
add_hydrogens=False,
sanitize=sanitize,
relax_iter=relax_iter,
largest_frag=largest_frag)
if mol is not None:
molecules.append(mol)
return molecules
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=Path, default='checkpoints/crossdocked_fullatom_cond.ckpt')
parser.add_argument('--pdbfile', type=str, default='example/5ndu.pdb')
parser.add_argument('--ref_ligand', type=str, default='example/5ndu_linked_mols.sdf')
parser.add_argument('--objective', type=str, default='sa', choices={'qed', 'sa'})
parser.add_argument('--timesteps', type=int, default=100)
parser.add_argument('--population_size', type=int, default=100)
parser.add_argument('--evolution_steps', type=int, default=10)
parser.add_argument('--top_k', type=int, default=7)
parser.add_argument('--outfile', type=Path, default='output.sdf')
parser.add_argument('--relax', action='store_true')
args = parser.parse_args()
pdb_id = Path(args.pdbfile).stem
device = 'cuda' if torch.cuda.is_available() else 'cpu'
population_size = args.population_size
evolution_steps = args.evolution_steps
top_k = args.top_k
# Load model
model = LigandPocketDDPM.load_from_checkpoint(
args.checkpoint, map_location=device)
model = model.to(device)
# Prepare ligand + pocket
# Load PDB
pdb_model = PDBParser(QUIET=True).get_structure('', args.pdbfile)[0]
# Define pocket based on reference ligand
residues = utils.get_pocket_from_ligand(pdb_model, args.ref_ligand)
pocket = model.prepare_pocket(residues, repeats=population_size)
if args.objective == 'qed':
objective_function = MoleculeProperties().calculate_qed
elif args.objective == 'sa':
objective_function = MoleculeProperties().calculate_sa
else:
### IMPLEMENT YOUR OWN OBJECTIVE
### FUNCTIONS HERE
raise ValueError(f"Objective function {args.objective} not recognized.")
ref_mol = Chem.SDMolSupplier(args.ref_ligand)[0]
# Store molecules in history dataframe
buffer = pd.DataFrame(columns=['generation', 'score', 'fate' 'mol', 'smiles'])
# Population initialization
buffer = buffer.append({'generation': 0,
'score': objective_function(ref_mol),
'fate': 'initial', 'mol': ref_mol,
'smiles': Chem.MolToSmiles(ref_mol)}, ignore_index=True)
for generation_idx in range(evolution_steps):
if generation_idx == 0:
molecules = buffer['mol'].tolist() * population_size
else:
# Select top k molecules from previous generation
previous_gen = buffer[buffer['generation'] == generation_idx]
top_k_molecules = previous_gen.nlargest(top_k, 'score')['mol'].tolist()
molecules = top_k_molecules * (population_size // top_k)
# Update the fate of selected top k molecules in the buffer
buffer.loc[buffer['generation'] == generation_idx, 'fate'] = 'survived'
# Ensure the right number of molecules
if len(molecules) < population_size:
molecules += [random.choice(molecules) for _ in range(population_size - len(molecules))]
# Diversify molecules
assert len(molecules) == population_size, f"Wrong number of molecules: {len(molecules)} when it should be {population_size}"
print(f"Generation {generation_idx}, mean score: {np.mean([objective_function(mol) for mol in molecules])}")
molecules = diversify_ligands(model,
pocket,
molecules,
timesteps=args.timesteps,
sanitize=True,
relax_iter=(200 if args.relax else 0))
# Evaluate and save molecules
for mol in molecules:
buffer = buffer.append({'generation': generation_idx + 1,
'score': objective_function(mol),
'fate': 'purged',
'mol': mol,
'smiles': Chem.MolToSmiles(mol)}, ignore_index=True)
# Make SDF files
utils.write_sdf_file(args.outfile, molecules)
# Save buffer
buffer.drop(columns=['mol'])
buffer.to_csv(args.outfile.with_suffix('.csv'))