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978fed5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | """Data preprocessing: SMILES -> Morgan fingerprints + RDKit descriptors."""
import logging
import os
import pickle
# Suppress RDKit warnings
logging.getLogger("rdkit").setLevel(logging.ERROR)
os.environ["RDKIT_VERBOSE"] = "0"
from pathlib import Path
import numpy as np
import pandas as pd
from utils import RANDOM_STATE, get_data_path
# RDKit 2D descriptors (≥20, validated to exist)
RDKIT_DESCRIPTOR_NAMES = [
"MolWt",
"MolLogP",
"NumHDonors",
"NumHAcceptors",
"NumRotatableBonds",
"NumValenceElectrons",
"NumRadicalElectrons",
"TPSA",
"LabuteASA",
"HeavyAtomMolWt",
"ExactMolWt",
"NumHeteroatoms",
"FractionCSP3",
"NumSaturatedRings",
"NumAliphaticRings",
"NumAromaticRings",
"RingCount",
"NumAmideBonds",
"NumSaturatedHeterocycles",
"NumSaturatedCarbocycles",
]
def _get_descriptor_list():
from rdkit.Chem import Descriptors
return [n for n in RDKIT_DESCRIPTOR_NAMES if hasattr(Descriptors, n)]
def _get_morgan_fingerprint(smiles: str, n_bits: int = 1024, radius: int = 2) -> np.ndarray | None:
"""Convert SMILES to Morgan fingerprint. Returns None if invalid."""
try:
from rdkit import Chem
from rdkit.Chem import AllChem
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return None
fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=n_bits)
return np.array(fp, dtype=np.float32)
except Exception:
return None
def _get_rdkit_descriptors(smiles: str, desc_names: list) -> np.ndarray | None:
"""Compute RDKit 2D descriptors. Returns None if invalid."""
try:
from rdkit import Chem
from rdkit.Chem import Descriptors
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return None
desc = [getattr(Descriptors, n)(mol) for n in desc_names]
arr = np.array(desc, dtype=np.float32)
if np.any(np.isnan(arr)) or np.any(np.isinf(arr)):
return None
return arr
except Exception:
return None
def preprocess() -> tuple[np.ndarray, np.ndarray, list[str]]:
"""
Load CSV, compute Morgan (1024-bit) + descriptors, return (X, y, feature_names).
Drops invalid SMILES.
"""
df = pd.read_csv(get_data_path())
df = df.dropna(subset=["Canonical_Smiles"])
df = df[df["Canonical_Smiles"].astype(str).str.strip() != ""]
desc_names = _get_descriptor_list()
fps_list = []
descs_list = []
valid_idx = []
for i, row in df.iterrows():
smi = str(row["Canonical_Smiles"]).strip()
fp = _get_morgan_fingerprint(smi)
desc = _get_rdkit_descriptors(smi, desc_names)
if fp is not None and desc is not None:
fps_list.append(fp)
descs_list.append(desc)
valid_idx.append(i)
fps = np.array(fps_list, dtype=np.float32)
descs = np.array(descs_list, dtype=np.float32)
X = np.hstack([fps, descs])
y = df.loc[valid_idx, "Activity"].values
feature_names = [f"morgan_{i}" for i in range(1024)] + desc_names
return X, y, feature_names
def save_preprocessed(output_dir: Path) -> tuple[np.ndarray, np.ndarray, list[str]]:
"""Preprocess, save to data/, return (X, y, feature_names)."""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
X, y, names = preprocess()
np.save(output_dir / "features.npy", X)
with open(output_dir / "feature_names.pkl", "wb") as f:
pickle.dump(names, f)
np.save(output_dir / "labels.npy", y)
return X, y, names
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