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utils.py
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utils.py
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from typing import Union, Iterable
import numpy as np
import torch
import torch.nn.functional as F
from rdkit import Chem
import networkx as nx
from networkx.algorithms import isomorphism
from Bio.PDB.Polypeptide import is_aa
class Queue():
def __init__(self, max_len=50):
self.items = []
self.max_len = max_len
def __len__(self):
return len(self.items)
def add(self, item):
self.items.insert(0, item)
if len(self) > self.max_len:
self.items.pop()
def mean(self):
return np.mean(self.items)
def std(self):
return np.std(self.items)
def reverse_tensor(x):
return x[torch.arange(x.size(0) - 1, -1, -1)]
#####
def get_grad_norm(
parameters: Union[torch.Tensor, Iterable[torch.Tensor]],
norm_type: float = 2.0) -> torch.Tensor:
"""
Adapted from: https://pytorch.org/docs/stable/_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = [p for p in parameters if p.grad is not None]
norm_type = float(norm_type)
if len(parameters) == 0:
return torch.tensor(0.)
device = parameters[0].grad.device
total_norm = torch.norm(torch.stack(
[torch.norm(p.grad.detach(), norm_type).to(device) for p in
parameters]), norm_type)
return total_norm
def write_xyz_file(coords, atom_types, filename):
out = f"{len(coords)}\n\n"
assert len(coords) == len(atom_types)
for i in range(len(coords)):
out += f"{atom_types[i]} {coords[i, 0]:.3f} {coords[i, 1]:.3f} {coords[i, 2]:.3f}\n"
with open(filename, 'w') as f:
f.write(out)
def write_sdf_file(sdf_path, molecules):
# NOTE Changed to be compatitble with more versions of rdkit
#with Chem.SDWriter(str(sdf_path)) as w:
# for mol in molecules:
# w.write(mol)
w = Chem.SDWriter(str(sdf_path))
w.SetKekulize(False)
for m in molecules:
if m is not None:
w.write(m)
# print(f'Wrote SDF file to {sdf_path}')
def residues_to_atoms(x_ca, atom_encoder):
x = x_ca
one_hot = F.one_hot(
torch.tensor(atom_encoder['C'], device=x_ca.device),
num_classes=len(atom_encoder)
).repeat(*x_ca.shape[:-1], 1)
return x, one_hot
def get_residue_with_resi(pdb_chain, resi):
res = [x for x in pdb_chain.get_residues() if x.id[1] == resi]
assert len(res) == 1
return res[0]
def get_pocket_from_ligand(pdb_model, ligand, dist_cutoff=8.0):
if ligand.endswith(".sdf"):
# ligand as sdf file
rdmol = Chem.SDMolSupplier(str(ligand))[0]
ligand_coords = torch.from_numpy(rdmol.GetConformer().GetPositions()).float()
resi = None
else:
# ligand contained in PDB; given in <chain>:<resi> format
chain, resi = ligand.split(':')
ligand = get_residue_with_resi(pdb_model[chain], int(resi))
ligand_coords = torch.from_numpy(
np.array([a.get_coord() for a in ligand.get_atoms()]))
pocket_residues = []
for residue in pdb_model.get_residues():
if residue.id[1] == resi:
continue # skip ligand itself
res_coords = torch.from_numpy(
np.array([a.get_coord() for a in residue.get_atoms()]))
if is_aa(residue.get_resname(), standard=True) \
and torch.cdist(res_coords, ligand_coords).min() < dist_cutoff:
pocket_residues.append(residue)
return pocket_residues
def batch_to_list(data, batch_mask):
# data_list = []
# for i in torch.unique(batch_mask):
# data_list.append(data[batch_mask == i])
# return data_list
# make sure batch_mask is increasing
idx = torch.argsort(batch_mask)
batch_mask = batch_mask[idx]
data = data[idx]
chunk_sizes = torch.unique(batch_mask, return_counts=True)[1].tolist()
return torch.split(data, chunk_sizes)
def num_nodes_to_batch_mask(n_samples, num_nodes, device):
assert isinstance(num_nodes, int) or len(num_nodes) == n_samples
if isinstance(num_nodes, torch.Tensor):
num_nodes = num_nodes.to(device)
sample_inds = torch.arange(n_samples, device=device)
return torch.repeat_interleave(sample_inds, num_nodes)
def rdmol_to_nxgraph(rdmol):
graph = nx.Graph()
for atom in rdmol.GetAtoms():
# Add the atoms as nodes
graph.add_node(atom.GetIdx(), atom_type=atom.GetAtomicNum())
# Add the bonds as edges
for bond in rdmol.GetBonds():
graph.add_edge(bond.GetBeginAtomIdx(), bond.GetEndAtomIdx())
return graph
def calc_rmsd(mol_a, mol_b):
""" Calculate RMSD of two molecules with unknown atom correspondence. """
graph_a = rdmol_to_nxgraph(mol_a)
graph_b = rdmol_to_nxgraph(mol_b)
gm = isomorphism.GraphMatcher(
graph_a, graph_b,
node_match=lambda na, nb: na['atom_type'] == nb['atom_type'])
isomorphisms = list(gm.isomorphisms_iter())
if len(isomorphisms) < 1:
return None
all_rmsds = []
for mapping in isomorphisms:
atom_types_a = [atom.GetAtomicNum() for atom in mol_a.GetAtoms()]
atom_types_b = [mol_b.GetAtomWithIdx(mapping[i]).GetAtomicNum()
for i in range(mol_b.GetNumAtoms())]
assert atom_types_a == atom_types_b
conf_a = mol_a.GetConformer()
coords_a = np.array([conf_a.GetAtomPosition(i)
for i in range(mol_a.GetNumAtoms())])
conf_b = mol_b.GetConformer()
coords_b = np.array([conf_b.GetAtomPosition(mapping[i])
for i in range(mol_b.GetNumAtoms())])
diff = coords_a - coords_b
rmsd = np.sqrt(np.mean(np.sum(diff * diff, axis=1)))
all_rmsds.append(rmsd)
if len(isomorphisms) > 1:
print("More than one isomorphism found. Returning minimum RMSD.")
return min(all_rmsds)
class AppendVirtualNodes:
def __init__(self, max_ligand_size, atom_encoder, symbol):
self.max_ligand_size = max_ligand_size
self.atom_encoder = atom_encoder
self.vidx = atom_encoder[symbol]
def __call__(self, data):
n_virt = self.max_ligand_size - data['num_lig_atoms']
mu = data['lig_coords'].mean(0, keepdim=True)
sigma = data['lig_coords'].std(0).max()
virt_coords = torch.randn(n_virt, 3) * sigma + mu
# insert virtual atom column
one_hot = torch.cat((data['lig_one_hot'][:, :self.vidx],
torch.zeros(data['num_lig_atoms'])[:, None],
data['lig_one_hot'][:, self.vidx:]), dim=1)
virt_one_hot = torch.zeros(n_virt, len(self.atom_encoder))
virt_one_hot[:, self.vidx] = 1
virt_mask = torch.ones(n_virt) * data['lig_mask'][0]
data['lig_coords'] = torch.cat((data['lig_coords'], virt_coords))
data['lig_one_hot'] = torch.cat((one_hot, virt_one_hot))
data['num_lig_atoms'] = self.max_ligand_size
data['lig_mask'] = torch.cat((data['lig_mask'], virt_mask))
data['num_virtual_atoms'] = n_virt
return data