§12.3-12.4 first binding result on ARM Mac.
- Toolchain SOLVED: AutoDock Vina 1.2.5 mac binary (Rosetta) + open-babel
(brew). No conda, no MLX. dock_positive_controls.py runs end-to-end.
- Cross-dock known binders + negatives into Hb (5E83) and PKR (8XFD),
box centered on co-crystal ligands (5L7=voxelotor, WV2=mitapivat).
Finding: raw Vina affinity ranks almost perfectly by MOLECULAR SIZE
(mitapivat > voxelotor > decitabine/caffeine > hydroxyurea) in both
pockets — mitapivat wins even on hemoglobin it doesn't target. Raw score
can't distinguish target-specific binding: the docking analog of the
connectivity specificity problem. Next: redocking-RMSD validation +
ligand-efficiency normalization.
Note: machine is 24GB (not 96GB per PLAN §2), capping local AF3-class
inference. tools/ gitignored (vina binary).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
