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inpaint.py
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inpaint.py
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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
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
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_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 inpaint_ligand(model, pdb_file, n_samples, ligand, fix_atoms,
add_n_nodes=None, center='ligand', sanitize=False,
largest_frag=False, relax_iter=0, timesteps=None,
resamplings=1, save_traj=False):
"""
Generate ligands given a pocket
Args:
model: Lightning model
pdb_file: PDB filename
n_samples: number of samples
ligand: reference ligand given in <chain>:<resi> format if the ligand is
contained in the PDB file, or path to an SDF file that
contains the ligand; used to define the pocket
fix_atoms: ligand atoms that should be fixed, e.g. "C1 N6 C5 C12"
center: 'ligand' or 'pocket'
add_n_nodes: number of ligand nodes to add, sampled randomly if 'None'
sanitize: whether to sanitize molecules or not
largest_frag: only return the largest fragment
relax_iter: number of force field optimization steps
timesteps: number of denoising steps, use training value if None
resamplings: number of resampling iterations
save_traj: save intermediate states to visualize a denoising trajectory
Returns:
list of molecules
"""
if save_traj and n_samples > 1:
raise NotImplementedError("Can only visualize trajectory with "
"n_samples=1.")
frames = timesteps if save_traj else 1
sanitize = False if save_traj else sanitize
relax_iter = 0 if save_traj else relax_iter
largest_frag = False if save_traj else largest_frag
# Load PDB
pdb_model = PDBParser(QUIET=True).get_structure('', pdb_file)[0]
# Define pocket based on reference ligand
residues = utils.get_pocket_from_ligand(pdb_model, ligand)
pocket = model.prepare_pocket(residues, repeats=n_samples)
# Get fixed ligand substructure
x_fixed, one_hot_fixed = prepare_substructure(ligand, fix_atoms, pdb_model)
n_fixed = len(x_fixed)
if add_n_nodes is None:
num_nodes_lig = model.ddpm.size_distribution.sample_conditional(
n1=None, n2=pocket['size'])
num_nodes_lig = torch.clamp(num_nodes_lig, min=n_fixed)
else:
num_nodes_lig = torch.ones(n_samples, dtype=int) * n_fixed + add_n_nodes
ligand_mask = utils.num_nodes_to_batch_mask(
len(num_nodes_lig), num_nodes_lig, model.device)
ligand = {
'x': torch.zeros((len(ligand_mask), model.x_dims),
device=model.device, dtype=FLOAT_TYPE),
'one_hot': torch.zeros((len(ligand_mask), model.atom_nf),
device=model.device, dtype=FLOAT_TYPE),
'size': num_nodes_lig,
'mask': ligand_mask
}
# fill in fixed atoms
lig_fixed = torch.zeros_like(ligand_mask)
for i in range(n_samples):
sele = (ligand_mask == i)
x_new = ligand['x'][sele]
x_new[:n_fixed] = x_fixed
ligand['x'][sele] = x_new
h_new = ligand['one_hot'][sele]
h_new[:n_fixed] = one_hot_fixed
ligand['one_hot'][sele] = h_new
fixed_new = lig_fixed[sele]
fixed_new[:n_fixed] = 1
lig_fixed[sele] = fixed_new
# Pocket's center of mass
pocket_com_before = scatter_mean(pocket['x'], pocket['mask'], dim=0)
# Run sampling
xh_lig, xh_pocket, lig_mask, pocket_mask = model.ddpm.inpaint(
ligand, pocket, lig_fixed, center=center,
resamplings=resamplings, timesteps=timesteps, return_frames=frames)
# Treat intermediate states as molecules for downstream processing
if save_traj:
xh_lig = utils.reverse_tensor(xh_lig)
xh_pocket = utils.reverse_tensor(xh_pocket)
lig_mask = torch.arange(xh_lig.size(0), device=model.device
).repeat_interleave(len(lig_mask))
pocket_mask = torch.arange(xh_pocket.size(0), device=model.device
).repeat_interleave(len(pocket_mask))
xh_lig = xh_lig.view(-1, xh_lig.size(2))
xh_pocket = xh_pocket.view(-1, xh_pocket.size(2))
# Move generated molecule back to the original pocket position
pocket_com_after = scatter_mean(xh_pocket[:, :model.x_dims], pocket_mask, dim=0)
xh_pocket[:, :model.x_dims] += \
(pocket_com_before - pocket_com_after)[pocket_mask]
xh_lig[:, :model.x_dims] += \
(pocket_com_before - pocket_com_after)[lig_mask]
# Build mol objects
x = xh_lig[:, :model.x_dims].detach().cpu()
atom_type = xh_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)
parser.add_argument('--pdbfile', type=str)
parser.add_argument('--ref_ligand', type=str, default=None)
parser.add_argument('--fix_atoms', type=str, nargs='+', default=None)
parser.add_argument('--center', type=str, default='ligand', choices={'ligand', 'pocket'})
parser.add_argument('--outfile', type=Path)
parser.add_argument('--n_samples', type=int, default=20)
parser.add_argument('--add_n_nodes', type=int, default=None)
parser.add_argument('--relax', action='store_true')
parser.add_argument('--sanitize', action='store_true')
parser.add_argument('--resamplings', type=int, default=20)
parser.add_argument('--timesteps', type=int, default=50)
parser.add_argument('--save_traj', action='store_true')
args = parser.parse_args()
pdb_id = Path(args.pdbfile).stem
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Load model
model = LigandPocketDDPM.load_from_checkpoint(
args.checkpoint, map_location=device)
model = model.to(device)
molecules = inpaint_ligand(model, args.pdbfile, args.n_samples,
args.ref_ligand, args.fix_atoms,
args.add_n_nodes, center=args.center,
sanitize=args.sanitize,
largest_frag=False,
relax_iter=(200 if args.relax else 0),
timesteps=args.timesteps,
resamplings=args.resamplings,
save_traj=args.save_traj)
# Make SDF files
utils.write_sdf_file(args.outfile, molecules)