Structure-binding track: scaffold + ligand-retrieval baseline

Start the structure-based binding branch (PLAN §12), baseline-first.

- src/binding.py: validated RDKit ligand retrieval (morgan_fp, tanimoto,
  retrieve_nearest = the §12.9 engine) + dock() stub documenting the
  blocked ARM-Mac toolchain
- scripts/binding_ligand_baseline.py: 300 drugs vs known binders
- docs/structure_binding_notes.md: status, toolchain blocker, next steps
- pyproject: [structure] extra (rdkit); data/raw/structures/ for PDBs

Step-0 finding: retrieval engine VALIDATED on in-set classes
(decitabine->azacitidine 0.62; vorinostat->scriptaid/belinostat) but the
distinctive binders voxelotor/mitapivat have no analog in our 300-drug
set (Tanimoto ~0.2). Needs (a) bigger library, (b) real docking (§12.3),
which is blocked on the ARM-Mac docking toolchain (§12.6 pitfall 4).
Structures 5E83 (Hb+voxelotor) and 8XFD (PKR+mitapivat) fetched.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/binding_ligand_baseline.py

@@ -0,0 +1,66 @@
+"""Structure-based track, step 0: ligand-based retrieval baseline (PLAN §12.9 engine).
+
+Docking (§12.3) needs a toolchain that doesn't pip-install on ARM Mac (AutoDock Vina) — that's the
+next dependency to solve. Meanwhile this runs now with pure RDKit: do any of our 300 drugs sit near
+the KNOWN sickle binders (voxelotor, mitapivat, decitabine) in chemical space? This is the
+retrieval engine §12.9 would point a generative beacon at, and a sanity check on the ligand data.
+
+NOT docking and NOT a binding claim — chemical similarity only. Similarity != activity (§12.9).
+"""
+
+from __future__ import annotations
+
+import pandas as pd
+import requests
+from rdkit import Chem, DataStructs, RDLogger
+from rdkit.Chem import rdFingerprintGenerator
+
+RDLogger.DisableLog("rdApp.*")
+MORGAN = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
+
+# Known sickle binders = positive-control beacons (target in parens).
+BINDERS = ["voxelotor", "mitapivat", "decitabine", "vorinostat"]
+
+
+def pubchem_smiles(name: str) -> str | None:
+    for prop in ("SMILES", "ConnectivitySMILES", "IsomericSMILES", "CanonicalSMILES"):
+        try:
+            u = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/{name}/property/{prop}/JSON"
+            d = requests.get(u, timeout=30).json()["PropertyTable"]["Properties"][0]
+            if prop in d:
+                return d[prop]
+        except Exception:
+            continue
+    return None
+
+
+def fp(smi: str):
+    if not isinstance(smi, str) or smi in ("-666", ""):
+        return None
+    m = Chem.MolFromSmiles(smi)
+    return MORGAN.GetFingerprint(m) if m else None
+
+
+def main() -> None:
+    binder_smi = {b: pubchem_smiles(b) for b in BINDERS}
+    print("known-binder SMILES:", {k: (v[:34] + "..." if v else "MISSING") for k, v in binder_smi.items()})
+
+    drugs = pd.read_csv("data/processed/drug_set_v1.csv")[["pert_iname", "canonical_smiles", "inclusion_reason"]]
+    reason = dict(zip(drugs.pert_iname, drugs.inclusion_reason))
+    drug_fp = {r.pert_iname: fp(r.canonical_smiles) for r in drugs.itertuples()}
+    drug_fp = {k: v for k, v in drug_fp.items() if v is not None}
+    print(f"fingerprinted {len(drug_fp)}/{len(drugs)} drugs\n")
+
+    for b, smi in binder_smi.items():
+        bfp = fp(smi)
+        if bfp is None:
+            print(f"{b}: no SMILES\n"); continue
+        sims = sorted(((DataStructs.TanimotoSimilarity(bfp, v), k) for k, v in drug_fp.items()), reverse=True)
+        print(f"nearest drugs to {b}:")
+        for s, k in sims[:5]:
+            print(f"    {s:.3f}  {k:22s} [{reason.get(k,'?')}]")
+        print()
+
+
+if __name__ == "__main__":
+    main()