File size: 4,246 Bytes
c289d87 | 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 | from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Iterable, List, Optional
import numpy as np
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem, Descriptors
from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator
from .graph_builders import build_ligand_graph
from .schemas import LigandEncoding
@dataclass
class LigandEncoderConfig:
radius: int = 2
n_bits: int = 1024
generate_3d: bool = False
class LigandEncoder:
"""Encode ligands from SMILES/SDF into vectors and graph objects."""
def __init__(self, config: Optional[LigandEncoderConfig] = None) -> None:
self.config = config or LigandEncoderConfig()
self._fp_gen = GetMorganGenerator(radius=self.config.radius, fpSize=self.config.n_bits)
def _mol_from_smiles(self, smiles: str):
mol = Chem.MolFromSmiles(smiles)
if mol is None:
raise ValueError(f"Invalid SMILES: {smiles}")
mol = Chem.AddHs(mol)
if self.config.generate_3d:
params = AllChem.ETKDGv3()
params.randomSeed = 42
status = AllChem.EmbedMolecule(mol, params)
if status == 0:
AllChem.UFFOptimizeMolecule(mol)
return mol
def _fingerprint(self, mol) -> np.ndarray:
fp = self._fp_gen.GetFingerprint(mol)
return np.asarray(fp, dtype=float)
def _descriptors(self, mol) -> Dict[str, float]:
return {
"mw": float(Descriptors.MolWt(mol)),
"logp": float(Descriptors.MolLogP(mol)),
"hbd": float(Descriptors.NumHDonors(mol)),
"hba": float(Descriptors.NumHAcceptors(mol)),
"tpsa": float(Descriptors.TPSA(mol)),
"rot_bonds": float(Descriptors.NumRotatableBonds(mol)),
"ring_count": float(Descriptors.RingCount(mol)),
}
def encode_smiles(self, ligand_id: str, smiles: str) -> LigandEncoding:
mol = self._mol_from_smiles(smiles)
fp = self._fingerprint(mol)
desc = self._descriptors(mol)
graph = build_ligand_graph(mol)
desc_vec = np.asarray(list(desc.values()), dtype=float)
vector = np.concatenate([fp, desc_vec])
prep = {
"canonical_smiles": Chem.MolToSmiles(Chem.RemoveHs(mol), canonical=True),
"formula": str(Descriptors.rdMolDescriptors.CalcMolFormula(mol)),
"mw": float(desc["mw"]),
}
return LigandEncoding(
ligand_id=ligand_id,
smiles=smiles,
fingerprint=fp,
descriptors=desc,
graph=graph,
vector=vector,
prep=prep,
)
def encode_table(self, ligands: pd.DataFrame) -> List[LigandEncoding]:
rows = []
for row in ligands.itertuples(index=False):
rows.append(self.encode_smiles(str(row.ligand_id), str(row.smiles)))
return rows
def encode_sdf(self, sdf_path: str | Path) -> List[LigandEncoding]:
suppl = Chem.SDMolSupplier(str(sdf_path), removeHs=False)
output: List[LigandEncoding] = []
for idx, mol in enumerate(suppl):
if mol is None:
continue
mol_h = Chem.AddHs(Chem.RemoveHs(mol))
lig_id = mol_h.GetProp("_Name") if mol_h.HasProp("_Name") else f"lig_{idx:04d}"
smiles = Chem.MolToSmiles(Chem.RemoveHs(mol_h), canonical=True)
fp = self._fingerprint(mol_h)
desc = self._descriptors(mol_h)
graph = build_ligand_graph(mol_h)
vector = np.concatenate([fp, np.asarray(list(desc.values()), dtype=float)])
prep = {
"canonical_smiles": smiles,
"formula": str(Descriptors.rdMolDescriptors.CalcMolFormula(mol_h)),
"mw": float(desc["mw"]),
}
output.append(
LigandEncoding(
ligand_id=lig_id,
smiles=smiles,
fingerprint=fp,
descriptors=desc,
graph=graph,
vector=vector,
prep=prep,
)
)
return output
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