# Structure-based binding track — working notes

Branch `structure-based-binding`. Implements PLAN §12. Baseline-first, start with the two cleanest
targets (Hemoglobin + PKR), de-risk the harness before scaling.

## Status (2026-06-23)

**Toolchain check (PLAN §12.6 pitfall 4, confirmed real):**
- ✅ RDKit installs on ARM Mac — ligand side ready.
- ❌ AutoDock Vina does NOT pip-install on ARM Mac; no docking binary available. Docking (§12.3)
  is **blocked on toolchain** — must resolve via conda/micromamba (`vina`/`smina`), a GPU AF3-class
  model (Boltz-2/Chai-1/DiffDock), or an x86 Vina binary under Rosetta.

**Structures obtained:** `5E83` (hemoglobin + voxelotor), `8XFD` (PKR + mitapivat) in
`data/raw/structures/`.

**Step 0 — ligand-based retrieval baseline (`scripts/binding_ligand_baseline.py`):**
RDKit Tanimoto of our 300 drugs vs known sickle binders.
- Engine VALIDATED on in-set classes: `decitabine`→azacitidine (0.62); `vorinostat`→scriptaid
  (0.42), belinostat (0.28). Correctly clusters DNMT1 / HDAC HbF-inducers.
- But voxelotor / mitapivat have **no analog** in our set (max Tanimoto ~0.20–0.26). A 300-drug
  library is too sparse to contain look-alikes of distinctive scaffolds.

**Takeaways:**
1. Ligand retrieval works but needs a **bigger drug library** to be useful for distinctive targets.
2. The targets without in-set analogs (Hb, PKR) need **actual docking** (§12.3) — which scores
   binding directly, no look-alike required. That is the gating next step, and it needs the
   toolchain solved.

## Next steps
- [ ] Resolve the docking toolchain (recommend: micromamba + smina/vina, CPU, no GPU needed for baseline).
- [ ] Dock the known binders (voxelotor→5E83, mitapivat→8XFD) as positive controls (§12.4 recovery test).
- [ ] Expand the ligand library (full ChEMBL/LINCS) for retrieval to have reach.
- [ ] Only then: AF3-class co-folding (Boltz-2/DiffDock) vs the docking baseline; and §12.9 generative beacon.