Reverso/docs/structure_binding_notes.md

# 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.

## Step 1 — docking baseline (2026-06-24)

**Toolchain SOLVED on ARM Mac:** AutoDock Vina 1.2.5 mac binary (`tools/vina`, runs under Rosetta)
+ open-babel (brew) for prep. Docking runs end-to-end (`scripts/dock_positive_controls.py`).
Co-crystal ligands identified: 5L7 = voxelotor (5E83), WV2 = mitapivat (8XFD).

**Positive-control cross-docking — inconclusive, and instructively so.** Affinities (kcal/mol):

| ligand | hemoglobin | PKR |
|---|---|---|
| voxelotor | −8.1 | −9.3 |
| mitapivat | −10.0 | −11.2 |
| decitabine | −6.6 | −7.0 |
| hydroxyurea | −3.9 | −3.6 |
| caffeine | −6.1 | −6.4 |

The scores rank almost perfectly by **molecular size** (mitapivat > voxelotor > decitabine/caffeine
> hydroxyurea) in *both* pockets — mitapivat wins even on hemoglobin, which it doesn't target. So
raw Vina affinity is confounded by ligand size and per-pocket stickiness; it cannot yet
distinguish target-specific binding. This is the **docking analog of the connectivity specificity
problem** — raw scores carry a systematic bias (size here, broadness there) that masquerades as
signal. voxelotor *does* dock to Hb (−8.1, a real score); the cross-target test just isn't the
right validation.

## Step 2 — redocking-RMSD validation FAILS across the board (2026-06-24)

Redocked each co-crystal ligand into its own structure (`scripts/dock_validate.py`); RMSD vs
crystal pose via obrms:

| redock | RMSD | note |
|---|---|---|
| voxelotor → Hb (5E83) | NA | covalent binder (Schiff base, αVal1) — out of scope §12.7 |
| mitapivat → PKR (8XFD) | 8.2 Å | allosteric, cofactor (FBP/Mg) stripped |
| **vorinostat → HDAC2 (4LXZ, Zn kept)** | **7.9 Å** | classical non-covalent target — should have worked |

**The clean target also failing means this is a systematic PIPELINE-QUALITY problem, not target
choice.** The cheap Vina + open-babel setup produces scores but does not reproduce known binding
geometry, so its affinities are not yet trustworthy. Ligand efficiency (affinity / heavy atoms)
also doesn't fix it — it over-corrects, ranking tiny hydroxyurea (−0.78) "best".

Likely causes (in priority order):
1. **Low-quality receptor prep** — open-babel `-xr` is not production docking prep. Need
   AutoDockTools `prepare_receptor` or **Meeko** + `reduce`/pdb2pqr for protonation, charges, and
   proper AutoDock atom typing.
2. **Ligand prep** — should use Meeko (correct rotatable bonds / typing), not bare obabel `--gen3d`.
3. **RMSD metric** — obrms superimposes before RMSD; redocking validation wants symmetry-corrected
   RMSD **in place** (receptor frame). Worth confirming with an in-place metric.

**Honest takeaway:** consistent with the whole project — the *quick* version of each method runs
but doesn't survive honest validation. Credible structure-based docking needs production prep
tooling (Meeko/ADFR), which is the real next investment for this track.

## Next steps
- [ ] Install **Meeko** (+ reduce / pdb2pqr) and redo receptor+ligand prep; re-run redocking RMSD.
- [ ] Fix the RMSD metric (in-place, symmetry-corrected) to rule out a measurement artifact.
- [ ] Only once redocking validates (<2 Å) are affinity scores trustworthy — then cross-dock /
  screen the library and revisit ligand-efficiency / pose-based scoring.
- [ ] Later: AF3-class co-folding (Boltz-2/DiffDock via PyTorch-MPS — 24 GB ceiling) and the §12.9
  generative beacon.

> **Hardware note:** this machine is **24 GB** unified memory (not the 96 GB PLAN §2 assumed),
> which caps local AF3-class model inference. Classical docking (above) is unaffected.