Update scripts/compute_embeddings_compound.py

scripts/compute_embeddings_compound.py

@@ -1,21 +1,3 @@
-"""
-compute_embeddings_compound.py
-──────────────────────────────
-Build a compound library (alcohols, aromatics, heterocycles + controls)
-and embed SMILES strings with ChemBERTa (seyonec/ChemBERTa-77M-MTR, 768-dim).
-
-Outputs
--------
-data/processed/ligand.csv          [compound, smiles, class, d0..d767]
-data/processed/ligand_manifest.csv provenance
-
-Usage
------
-  python scripts/compute_embeddings_compound.py [--mock]
-
-  --mock  Use random embeddings instead of ChemBERTa (for offline testing).
-"""
-
 import argparse
 from pathlib import Path
 
@@ -31,7 +13,6 @@ DEVICE     = "cuda" if torch.cuda.is_available() else "cpu"
 CHEMBERTA  = "seyonec/ChemBERTa-77M-MTR"     # 768-dim
 
 
-# ── Compound library ──────────────────────────────────────────────────────────
 
 CONTROLS = [("ETHANOL", "CCO"), ("H2O2", "OO")]
 
@@ -79,7 +60,6 @@ def build_library() -> pd.DataFrame:
     return df.reset_index(drop=True)
 
 
-# ── ChemBERTa embedding ───────────────────────────────────────────────────────
 
 def load_chemberta(model_name: str = CHEMBERTA):
     tok = AutoTokenizer.from_pretrained(model_name)
@@ -96,7 +76,6 @@ def embed_smiles(smiles: str, tok, mdl) -> np.ndarray:
     return out.last_hidden_state[:, 0, :].squeeze().cpu().numpy().astype(np.float32)
 
 
-# ── Sanitize SMILES with RDKit ────────────────────────────────────────────────
 
 def canonicalize(smiles: str) -> str:
     try:
@@ -108,7 +87,6 @@ def canonicalize(smiles: str) -> str:
         return smiles
 
 
-# ── Main ──────────────────────────────────────────────────────────────────────
 
 def main(mock: bool = False):
     print(f"Device: {DEVICE}  |  mock={mock}")
@@ -142,8 +120,8 @@ def main(mock: bool = 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")
+    print(f"\n Saved ligand.csv  ({len(ligand_df)} compounds, d={d_lig})")
+    print(f" Saved ligand_manifest.csv")
 
 
 if __name__ == "__main__":