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generate_with_protein.py
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generate_with_protein.py
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import argparse
import os
import numpy as np
import torch
import subprocess
from rdkit import Chem
from Bio.PDB import PDBParser
from src import const
from src.datasets import (
collate_with_fragment_without_pocket_edges, get_dataloader, get_one_hot, parse_molecule, MOADDataset
)
from src.lightning import DDPM
from src.visualizer import save_xyz_file
from src.utils import FoundNaNException, set_deterministic
from tqdm import tqdm
from src.linker_size_lightning import SizeClassifier
from pdb import set_trace
parser = argparse.ArgumentParser()
parser.add_argument(
'--fragments', action='store', type=str, required=True,
help='Path to the file with input fragments'
)
parser.add_argument(
'--protein', action='store', type=str, required=True,
help='Path to the file with the target protein'
)
parser.add_argument(
'--backbone_atoms_only', action='store_true', required=False, default=False,
help='Flag if to use only protein backbone atoms'
)
parser.add_argument(
'--model', action='store', type=str, required=True,
help='Path to the DiffLinker model'
)
parser.add_argument(
'--linker_size', action='store', type=str, required=True,
help='Linker size (int) or allowed size boundaries (comma-separated integers) or path to the size prediction model'
)
parser.add_argument(
'--output', action='store', type=str, required=False, default='./',
help='Directory where sampled molecules will be saved'
)
parser.add_argument(
'--n_samples', action='store', type=int, required=False, default=5,
help='Number of linkers to generate'
)
parser.add_argument(
'--n_steps', action='store', type=int, required=False, default=None,
help='Number of denoising steps'
)
parser.add_argument(
'--anchors', action='store', type=str, required=False, default=None,
help='Comma-separated indices of anchor atoms '
'(according to the order of atoms in the input fragments file, enumeration starts with 1)'
)
parser.add_argument(
'--max_batch_size', action='store', type=int, required=False, default=64,
help='Max batch size'
)
parser.add_argument(
'--random_seed', action='store', type=int, required=False, default=None,
help='Random seed'
)
def read_molecule(path):
if path.endswith('.pdb'):
return Chem.MolFromPDBFile(path, sanitize=False, removeHs=True)
elif path.endswith('.mol'):
return Chem.MolFromMolFile(path, sanitize=False, removeHs=True)
elif path.endswith('.mol2'):
return Chem.MolFromMol2File(path, sanitize=False, removeHs=True)
elif path.endswith('.sdf'):
return Chem.SDMolSupplier(path, sanitize=False, removeHs=True)[0]
raise Exception('Unknown file extension')
def get_pocket(mol, pdb_path, backbone_atoms_only=False):
struct = PDBParser().get_structure('', pdb_path)
residue_ids = []
atom_coords = []
for residue in struct.get_residues():
resid = residue.get_id()[1]
for atom in residue.get_atoms():
atom_coords.append(atom.get_coord())
residue_ids.append(resid)
residue_ids = np.array(residue_ids)
atom_coords = np.array(atom_coords)
mol_atom_coords = mol.GetConformer().GetPositions()
distances = np.linalg.norm(atom_coords[:, None, :] - mol_atom_coords[None, :, :], axis=-1)
contact_residues = np.unique(residue_ids[np.where(distances.min(1) <= 6)[0]])
pocket_coords_full = []
pocket_types_full = []
pocket_coords_bb = []
pocket_types_bb = []
for residue in struct.get_residues():
resid = residue.get_id()[1]
if resid not in contact_residues:
continue
for atom in residue.get_atoms():
atom_name = atom.get_name()
atom_type = atom.element.upper()
atom_coord = atom.get_coord()
pocket_coords_full.append(atom_coord.tolist())
pocket_types_full.append(atom_type)
if atom_name in {'N', 'CA', 'C', 'O'}:
pocket_coords_bb.append(atom_coord.tolist())
pocket_types_bb.append(atom_type)
pocket_pos = []
pocket_one_hot = []
pocket_charges = []
generator = (
zip(pocket_coords_bb, pocket_types_bb)
if backbone_atoms_only
else zip(pocket_coords_full, pocket_types_full)
)
for coord, atom_type in generator:
if atom_type not in const.GEOM_ATOM2IDX.keys():
continue
pocket_pos.append(coord)
pocket_one_hot.append(get_one_hot(atom_type, const.GEOM_ATOM2IDX))
pocket_charges.append(const.GEOM_CHARGES[atom_type])
pocket_pos = np.array(pocket_pos)
pocket_one_hot = np.array(pocket_one_hot)
pocket_charges = np.array(pocket_charges)
return pocket_pos, pocket_one_hot, pocket_charges
def main(input_path, protein_path, backbone_atoms_only, model,
output_dir, n_samples, n_steps, linker_size, anchors, max_batch_size, random_seed):
# Setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(output_dir, exist_ok=True)
if random_seed is not None:
set_deterministic(random_seed)
if linker_size.isdigit():
print(f'Will generate linkers with {linker_size} atoms')
linker_size = int(linker_size)
def sample_fn(_data):
return torch.ones(_data['positions'].shape[0], device=device, dtype=const.TORCH_INT) * linker_size
else:
boundaries = [x.strip() for x in linker_size.split(',')]
if len(boundaries) == 2 and boundaries[0].isdigit() and boundaries[1].isdigit():
left = int(boundaries[0])
right = int(boundaries[1])
print(f'Will generate linkers with numbers of atoms sampled from U({left}, {right})')
def sample_fn(_data):
shape = len(_data['positions']),
return torch.randint(left, right + 1, shape, device=device, dtype=const.TORCH_INT)
else:
print(f'Will generate linkers with sampled numbers of atoms')
size_nn = SizeClassifier.load_from_checkpoint(linker_size, map_location=device).eval().to(device)
def sample_fn(_data):
out, _ = size_nn.forward(_data, return_loss=False, with_pocket=True, adjust_shape=True)
probabilities = torch.softmax(out, dim=1)
distribution = torch.distributions.Categorical(probs=probabilities)
samples = distribution.sample()
sizes = []
for label in samples.detach().cpu().numpy():
sizes.append(size_nn.linker_id2size[label])
sizes = torch.tensor(sizes, device=samples.device, dtype=const.TORCH_INT)
return sizes
ddpm = DDPM.load_from_checkpoint(model, map_location=device).eval().to(device)
if n_steps is not None:
ddpm.edm.T = n_steps
if ddpm.center_of_mass == 'anchors' and anchors is None:
print(
'Please pass anchor atoms indices '
'or use another DiffLinker model that does not require information about anchors'
)
return
# Reading input fragments
extension = input_path.split('.')[-1]
if extension not in ['sdf', 'pdb', 'mol', 'mol2']:
print('Please upload the fragments file in one of the following formats: .pdb, .sdf, .mol, .mol2')
return
protein_extension = protein_path.split('.')[-1]
if protein_extension != 'pdb':
print('Please upload the protein file in .pdb format')
return
try:
molecule = read_molecule(input_path)
molecule = Chem.RemoveAllHs(molecule)
name = '.'.join(input_path.split('/')[-1].split('.')[:-1])
except Exception as e:
return f'Could not read the file with fragments: {e}'
# Parsing fragments data
frag_pos, frag_one_hot, frag_charges = parse_molecule(molecule, is_geom=ddpm.is_geom)
# Parsing pocket data
try:
pocket_pos, pocket_one_hot, pocket_charges = get_pocket(molecule, protein_path, backbone_atoms_only)
except Exception as e:
return f'Could not read the file with pocket: {e}'
positions = np.concatenate([frag_pos, pocket_pos], axis=0)
one_hot = np.concatenate([frag_one_hot, pocket_one_hot], axis=0)
charges = np.concatenate([frag_charges, pocket_charges], axis=0)
anchor_flags = np.zeros_like(charges)
if anchors is not None:
for anchor in anchors.split(','):
anchor_flags[int(anchor.strip()) - 1] = 1
fragment_only_mask = np.concatenate([
np.ones_like(frag_charges),
np.zeros_like(pocket_charges),
])
pocket_mask = np.concatenate([
np.zeros_like(frag_charges),
np.ones_like(pocket_charges),
])
linker_mask = np.concatenate([
np.zeros_like(frag_charges),
np.zeros_like(pocket_charges),
])
fragment_mask = np.concatenate([
np.ones_like(frag_charges),
np.ones_like(pocket_charges),
])
dataset = [{
'uuid': '0',
'name': '0',
'positions': torch.tensor(positions, dtype=const.TORCH_FLOAT, device=device),
'one_hot': torch.tensor(one_hot, dtype=const.TORCH_FLOAT, device=device),
'charges': torch.tensor(charges, dtype=const.TORCH_FLOAT, device=device),
'anchors': torch.tensor(anchor_flags, dtype=const.TORCH_FLOAT, device=device),
'fragment_only_mask': torch.tensor(fragment_only_mask, dtype=const.TORCH_FLOAT, device=device),
'pocket_mask': torch.tensor(pocket_mask, dtype=const.TORCH_FLOAT, device=device),
'fragment_mask': torch.tensor(fragment_mask, dtype=const.TORCH_FLOAT, device=device),
'linker_mask': torch.tensor(linker_mask, dtype=const.TORCH_FLOAT, device=device),
'num_atoms': len(positions),
}] * n_samples
dataset = MOADDataset(data=dataset)
ddpm.val_dataset = dataset
global_batch_size = min(n_samples, max_batch_size)
dataloader = get_dataloader(
dataset, batch_size=global_batch_size, collate_fn=collate_with_fragment_without_pocket_edges
)
# Sampling
print('Sampling...')
for batch_i, data in tqdm(enumerate(dataloader), total=len(dataloader)):
batch_size = len(data['positions'])
chain = None
for i in range(5):
try:
chain, node_mask = ddpm.sample_chain(data, sample_fn=sample_fn, keep_frames=1)
break
except FoundNaNException:
continue
if chain is None:
raise Exception('Could not generate in 5 attempts')
x = chain[0][:, :, :ddpm.n_dims]
h = chain[0][:, :, ddpm.n_dims:]
# Put the molecule back to the initial orientation
com_mask = data['fragment_only_mask'] if ddpm.center_of_mass == 'fragments' else data['anchors']
pos_masked = data['positions'] * com_mask
N = com_mask.sum(1, keepdims=True)
mean = torch.sum(pos_masked, dim=1, keepdim=True) / N
x = x + mean * node_mask
offset_idx = batch_i * global_batch_size
names = [f'output_{offset_idx+i}_{name}' for i in range(batch_size)]
node_mask[torch.where(data['pocket_mask'])] = 0
save_xyz_file(output_dir, h, x, node_mask, names=names, is_geom=ddpm.is_geom, suffix='')
for i in range(batch_size):
out_xyz = f'{output_dir}/output_{offset_idx+i}_{name}_.xyz'
out_sdf = f'{output_dir}/output_{offset_idx+i}_{name}_.sdf'
subprocess.run(f'obabel {out_xyz} -O {out_sdf} 2> /dev/null', shell=True)
print(f'Saved generated molecules in .xyz and .sdf format in directory {output_dir}')
if __name__ == '__main__':
args = parser.parse_args()
main(
input_path=args.fragments,
protein_path=args.protein,
backbone_atoms_only=args.backbone_atoms_only,
model=args.model,
output_dir=args.output,
n_samples=args.n_samples,
n_steps=args.n_steps,
linker_size=args.linker_size,
anchors=args.anchors,
max_batch_size=args.max_batch_size,
random_seed=args.random_seed,
)