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9f9fb84 f7fae64 9f9fb84 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | import argparse
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from transformers import AutoModel, AutoTokenizer
from tqdm.auto import tqdm
DATA_PROC = Path("data/processed"); DATA_PROC.mkdir(parents=True, exist_ok=True)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
CHEMBERTA = "seyonec/ChemBERTa-77M-MTR" # 768-dim
CONTROLS = [("ETHANOL", "CCO"), ("H2O2", "OO")]
def _gen_alcohols(n=150):
lib = []
for c in range(1, 21):
lib.append((f"ALK_{c:02d}", "C" * c + "O"))
for c in range(3, 13):
lib.append((f"IALK_{c}", "C(C)" + "C" * (c - 2) + "O"))
return lib[:n]
def _gen_aromatics(n=200):
subs = ["Cl", "Br", "F", "N(=O)=O", "C(=O)O", "C#N", "OCC", "CCN", "CC(=O)O"]
cores = ["c1ccccc1", "c1ccc(cc1)"]
lib, k = [], 0
for s in subs:
for c in cores:
lib.append((f"ARO_{k:03d}", c + s)); k += 1
if k >= n: return lib
return lib
def _gen_heterocycles(n=200):
rings = ["c1ncccc1", "c1occcn1", "n1ccccc1", "c1ccncc1", "c1ccsc1", "c1ncncn1"]
lib, k = [], 0
for r in rings:
lib.append((f"HET_{k:03d}", r)); k += 1
lib.append((f"HETOH_{k:03d}", r + "O")); k += 1
if k >= n: break
while len(lib) < n:
lib.append((f"HETPAD_{len(lib):03d}", "c1ncncn1"))
return lib[:n]
def _classify(smiles: str) -> str:
if smiles == "CCO": return "solvent"
if smiles == "OO": return "oxidant"
if "c1" in smiles: return "aromatic/heterocycle"
if smiles.endswith("O"): return "alcohol"
return "other"
def build_library() -> pd.DataFrame:
lib = CONTROLS + _gen_alcohols(180) + _gen_aromatics(220) + _gen_heterocycles(210)
df = pd.DataFrame(lib, columns=["compound", "smiles"]).drop_duplicates("compound")
df["class"] = df["smiles"].map(_classify)
df["is_control"] = df["compound"].isin(["ETHANOL", "H2O2"])
return df.reset_index(drop=True)
def load_chemberta(model_name: str = CHEMBERTA):
tok = AutoTokenizer.from_pretrained(model_name)
mdl = AutoModel.from_pretrained(model_name).eval().to(DEVICE)
return tok, mdl
@torch.no_grad()
def embed_smiles(smiles: str, tok, mdl) -> np.ndarray:
"""Return CLS-token embedding as float32 array."""
inputs = tok(smiles, return_tensors="pt", truncation=True, max_length=512,
padding=True)
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
out = mdl(**inputs)
return out.last_hidden_state[:, 0, :].squeeze().cpu().numpy().astype(np.float32)
def canonicalize(smiles: str) -> str:
try:
from rdkit import Chem, RDLogger
RDLogger.DisableLog("rdApp.*")
mol = Chem.MolFromSmiles(smiles)
return Chem.MolToSmiles(mol) if mol else smiles
except Exception:
return smiles
def main(mock: bool = False):
print(f"Device: {DEVICE} | mock={mock}")
df_lib = build_library()
df_lib["smiles"] = df_lib["smiles"].map(canonicalize)
print(f"Library: {len(df_lib)} compounds")
if not mock:
tok, mdl = load_chemberta()
d_lig = 768
else:
d_lig = 768
rng = np.random.default_rng(42)
rows = []
for _, row in tqdm(df_lib.iterrows(), total=len(df_lib)):
if mock:
emb = rng.normal(0, 1, d_lig).astype(np.float32)
else:
try:
emb = embed_smiles(row["smiles"], tok, mdl)
except Exception as e:
print(f" ⚠ {row['compound']}: {e}; using zeros")
emb = np.zeros(d_lig, dtype=np.float32)
rows.append(emb)
emb_df = pd.DataFrame(rows, columns=[f"d{j}" for j in range(d_lig)])
ligand_df = pd.concat([df_lib, emb_df], axis=1)
ligand_df.to_csv(DATA_PROC / "ligand.csv", index=False)
# Manifest (no embeddings)
df_lib.to_csv(DATA_PROC / "ligand_manifest.csv", index=False)
print(f"\n Saved ligand.csv ({len(ligand_df)} compounds, d={d_lig})")
print(f" Saved ligand_manifest.csv")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--mock", action="store_true",
help="Use random embeddings (offline mode)")
args = parser.parse_args()
main(mock=args.mock)
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