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import numpy as np
import torch
from rdkit import Chem
from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator
import selfies as sf
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
mfpgen = GetMorganGenerator(
radius=2,
countSimulation=False,
includeChirality=False,
useBondTypes=True,
onlyNonzeroInvariants=False,
includeRingMembership=True,
countBounds=None,
fpSize=2048,
atomInvariantsGenerator=None,
bondInvariantsGenerator=None,
includeRedundantEnvironments=False,
)
def smiles_to_graph(smiles):
mol = Chem.MolFromSmiles(smiles) # type: ignore
if mol is None:
return None
return mol_to_graph(mol)
def selfies_to_graph(smiles_string):
try:
selfies_string = sf.encoder(smiles_string)
smiles = sf.decoder(selfies_string)
mol = Chem.MolFromSmiles(smiles) # type: ignore
if mol is None:
raise ValueError("Decoded SELFIES is invalid")
return mol_to_graph(mol)
except Exception:
fallback = smiles_to_graph(smiles_string)
if fallback is None:
return None
return fallback
def ecfp_to_graph(smiles_str: str, max_bits: int = 2048, k: int = 2) -> Data | None:
mol = Chem.MolFromSmiles(smiles_str) # type: ignore
if mol is None:
return None
fp = mfpgen.GetFingerprintAsNumPy(mol)
active_bits = np.nonzero(fp)[0]
n = len(active_bits)
if n == 0:
return None
edge_index = []
for i in range(n):
for j in range(i + 1, min(i + 1 + k, n)):
edge_index.append([i, j])
edge_index.append([j, i])
edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous()
x = torch.zeros((n, max_bits), dtype=torch.float)
for i, bit_idx in enumerate(active_bits):
x[i, bit_idx] = 1.0
return Data(x=x, edge_index=edge_index)
def mol_to_graph(mol):
atom_feats = []
for atom in mol.GetAtoms():
atom_feats.append(
[
atom.GetAtomicNum(),
atom.GetDegree(),
atom.GetFormalCharge(),
atom.GetIdx(),
]
)
x = torch.tensor(atom_feats, dtype=torch.float)
edge_index = []
edge_attr = []
for bond in mol.GetBonds():
i = bond.GetBeginAtomIdx()
j = bond.GetEndAtomIdx()
edge_index.append((i, j))
edge_index.append((j, i))
btype = bond.GetBondTypeAsDouble()
edge_attr.append([btype])
edge_attr.append([btype])
edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous()
edge_attr = torch.tensor(edge_attr, dtype=torch.float)
return Data(x=x, edge_index=edge_index, edge_attr=edge_attr)
def smiles_for_gp(smiles: str) -> np.ndarray:
mol = Chem.MolFromSmiles(smiles) # type: ignore
if mol is None:
return np.zeros(mfpgen.GetNumBits(), dtype=np.float32)
arr = mfpgen.GetFingerprintAsNumPy(mol)
return arr.astype(np.float32)
def selfies_for_gp(selfies_str, radius=2, n_bits=2048):
try:
smiles = sf.decoder(selfies_str)
assert isinstance(smiles, str)
return smiles_for_gp(smiles)
except:
return np.zeros(n_bits)
def ecfp_for_gp(smiles_str: str) -> np.ndarray:
mol = Chem.MolFromSmiles(smiles_str) # type: ignore
if mol is None:
return np.zeros(mfpgen.GetNumBits(), dtype=np.float32)
return mfpgen.GetFingerprintAsNumPy(mol).astype(np.float32)
def graph_native_loader(graph_list, batch_size=32, shuffle=True):
return DataLoader(graph_list, batch_size=batch_size, shuffle=shuffle)
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