Junior B.
75f5383961
§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>
66 lines
3.5 KiB
Markdown
66 lines
3.5 KiB
Markdown
# Structure-based binding track — working notes
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Branch `structure-based-binding`. Implements PLAN §12. Baseline-first, start with the two cleanest
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targets (Hemoglobin + PKR), de-risk the harness before scaling.
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## Status (2026-06-23)
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**Toolchain check (PLAN §12.6 pitfall 4, confirmed real):**
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- ✅ RDKit installs on ARM Mac — ligand side ready.
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- ❌ AutoDock Vina does NOT pip-install on ARM Mac; no docking binary available. Docking (§12.3)
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is **blocked on toolchain** — must resolve via conda/micromamba (`vina`/`smina`), a GPU AF3-class
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model (Boltz-2/Chai-1/DiffDock), or an x86 Vina binary under Rosetta.
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**Structures obtained:** `5E83` (hemoglobin + voxelotor), `8XFD` (PKR + mitapivat) in
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`data/raw/structures/`.
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**Step 0 — ligand-based retrieval baseline (`scripts/binding_ligand_baseline.py`):**
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RDKit Tanimoto of our 300 drugs vs known sickle binders.
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- Engine VALIDATED on in-set classes: `decitabine`→azacitidine (0.62); `vorinostat`→scriptaid
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(0.42), belinostat (0.28). Correctly clusters DNMT1 / HDAC HbF-inducers.
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- But voxelotor / mitapivat have **no analog** in our set (max Tanimoto ~0.20–0.26). A 300-drug
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library is too sparse to contain look-alikes of distinctive scaffolds.
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**Takeaways:**
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1. Ligand retrieval works but needs a **bigger drug library** to be useful for distinctive targets.
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2. The targets without in-set analogs (Hb, PKR) need **actual docking** (§12.3) — which scores
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binding directly, no look-alike required. That is the gating next step, and it needs the
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toolchain solved.
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## Step 1 — docking baseline (2026-06-24)
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**Toolchain SOLVED on ARM Mac:** AutoDock Vina 1.2.5 mac binary (`tools/vina`, runs under Rosetta)
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+ open-babel (brew) for prep. Docking runs end-to-end (`scripts/dock_positive_controls.py`).
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Co-crystal ligands identified: 5L7 = voxelotor (5E83), WV2 = mitapivat (8XFD).
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**Positive-control cross-docking — inconclusive, and instructively so.** Affinities (kcal/mol):
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| ligand | hemoglobin | PKR |
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|---|---|---|
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| voxelotor | −8.1 | −9.3 |
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| mitapivat | −10.0 | −11.2 |
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| decitabine | −6.6 | −7.0 |
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| hydroxyurea | −3.9 | −3.6 |
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| caffeine | −6.1 | −6.4 |
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The scores rank almost perfectly by **molecular size** (mitapivat > voxelotor > decitabine/caffeine
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> hydroxyurea) in *both* pockets — mitapivat wins even on hemoglobin, which it doesn't target. So
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raw Vina affinity is confounded by ligand size and per-pocket stickiness; it cannot yet
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distinguish target-specific binding. This is the **docking analog of the connectivity specificity
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problem** — raw scores carry a systematic bias (size here, broadness there) that masquerades as
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signal. voxelotor *does* dock to Hb (−8.1, a real score); the cross-target test just isn't the
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right validation.
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## Next steps
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- [ ] **Redocking-RMSD validation** (the gold-standard positive control): redock the crystal ligand
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5L7/WV2 into its own structure, compute pose RMSD vs crystal. <2 Å = geometry validated. This
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tests pose accuracy, which size bias doesn't corrupt.
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- [ ] **Ligand-efficiency normalization** (affinity / heavy-atom count) to de-bias the size effect,
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the docking counterpart of the connectivity calibration work.
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- [ ] Expand the ligand library (full ChEMBL/LINCS) for retrieval reach.
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- [ ] Only then: AF3-class co-folding (Boltz-2/DiffDock via PyTorch-MPS — note 24 GB ceiling) vs the
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docking baseline; and §12.9 generative beacon.
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> **Hardware note:** this machine is **24 GB** unified memory (not the 96 GB PLAN §2 assumed),
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> which caps local AF3-class model inference. Classical docking (above) is unaffected.
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