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.

Next steps

• Redocking-RMSD validation (the gold-standard positive control): redock the crystal ligand 5L7/WV2 into its own structure, compute pose RMSD vs crystal. <2 Å = geometry validated. This tests pose accuracy, which size bias doesn't corrupt.
• Ligand-efficiency normalization (affinity / heavy-atom count) to de-bias the size effect, the docking counterpart of the connectivity calibration work.
• Expand the ligand library (full ChEMBL/LINCS) for retrieval reach.
• Only then: AF3-class co-folding (Boltz-2/DiffDock via PyTorch-MPS — note 24 GB ceiling) vs the docking baseline; and §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.