Production docking: prep helps (7.9->4.8A) but Vina wrong tool for sickle

§12.4 pushed to its limit. Meeko ligand prep + in-place symmetry RMSD
(spyrmsd, not obrms) on clean HDAC2/vorinostat: 7.9A -> 4.76A. Prep and
metric mattered, but still FAIL.

Residual cause is fundamental: vorinostat binds via hydroxamate-Zn
chelation and Vina has no metal-coordination term. Real finding: sickle's
druggable targets bind via non-classical chemistry classical docking
handles poorly -- Hb (covalent), PKR (allosteric+cofactor), HDAC (Zn
chelation). Vina is the wrong tool for this target landscape.

Redirect: data-driven AF3-class co-folding (Boltz-2/Chai-1/DiffDock)
handles these modes -- the indicated next tool, gated by the 24GB local
memory ceiling (cloud GPU needed). The "GPU breaks all-local" §12.6
prediction is now the binding constraint of the track.

Adds: scripts/dock_production.py; deps meeko, spyrmsd, gemmi.

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

docs/structure_binding_notes.md

@@ -79,13 +79,35 @@ Likely causes (in priority order):
 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.
 
+## Step 3 — production prep helps, but classical docking is the wrong tool here (2026-06-24)
+
+`scripts/dock_production.py`: Meeko ligand prep (proper rotatable-bond/AD typing) + in-place
+symmetry-corrected RMSD (spyrmsd, not obrms which superimposes). On the clean HDAC2/vorinostat
+target (Zn kept):
+
+- **7.9 Å → 4.76 Å** with proper ligand prep + correct metric. Prep and metric genuinely mattered.
+- But still FAIL (>2 Å). The residual is the deeper problem: **vorinostat binding is defined by its
+  hydroxamate chelating the catalytic Zn, and Vina has no metal-coordination term** — it cannot
+  score the interaction that determines the pose.
+
+**The real finding: sickle's druggable targets bind via non-classical chemistry that classical
+docking handles poorly** — Hb/voxelotor (covalent), PKR/mitapivat (allosteric + cofactor),
+HDAC/vorinostat (Zn chelation). This is the target landscape, not bad luck. AutoDock Vina is the
+wrong tool for it.
+
+**Redirect:** the modality that DOES handle covalent/metal/induced-fit binding is **data-driven
+AF3-class co-folding** (Boltz-2 / Chai-1 / DiffDock — they learn these modes from the PDB). That is
+the indicated next tool for this disease — and it's gated by the **24 GB local memory ceiling**
+(PLAN §12.6 pitfall 4): needs a cloud GPU or a bigger box. The "GPU breaks all-local" prediction is
+now the binding constraint of the whole 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.
+- [ ] AF3-class co-folding on a GPU (Boltz-2 affinity / Chai-1 / DiffDock); redo the §12.4
+  positive-control recovery test there — it should handle the metal/covalent modes Vina can't.
+- [ ] (optional) Salvage one classical Vina case: PKR with FBP/Mg cofactors RETAINED, to confirm
+  the harness can validate on a non-metal sickle target.
+- [ ] Production receptor prep (Meeko mk_prepare_receptor + protonation) if staying with Vina.
+- [ ] §12.9 generative beacon — only after a validated scoring function exists.
 
 > **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.