§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>
Post-hoc improvement after the pre-registered v1 recovery test failed.
Two changes, diagnosing v1's failure:
- score on the full 12,328-gene LINCS space (week2_lincs_extract.py),
lifting signature overlap from 12% to 85% (brings erythroid markers in)
- src/scoring.py: KS connectivity + per-drug specificity z-score
(spec_z = SDs below a 1,000 random-query null). Primary ranking is
now spec_z. (Textbook tau saturated at +/-100 for a coherent query —
documented; needs a reference-signature library, a v2 item.)
- week3_scoring.py: spec_z primary + WTCS reference + prior-blended
- tests: tau/spec_z calibration test; 19 passing
- scripts/exp_genespace.py: the BING vs all-12,328 comparison
Result: hydroxyurea recovers (rank 40 -> 18, top 6%, passes top-10%),
confirming the v1 failure was the landmark bottleneck not the algorithm.
Overall STILL FAILS: L-glutamine does not reverse (rank 213, metabolite),
and negative controls (norethindrone, ciprofloxacin) rank top-3 —
connectivity != therapeutic relatedness. v1.1 is post-hoc/exploratory,
not a confirmatory test; reported as such in recovery_test_report.md.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Run the formal recovery test against the pre-registered criteria and
write the deliverable report (PLAN §6 Week 4):
- week4_recovery_test.py: evaluate hydroxyurea/L-glutamine + 5
pre-specified negative controls vs the committed criteria
- recovery_test_report.md: methodology, FAIL result with diagnosis,
top-10, lisinopril as the non-obvious candidate, limitations, v2
- known_limitations.md: L-glutamine coverage resolved, 12%-overlap
driver, recovery outcome table
Outcome: FAIL on all 3 criteria (hydroxyurea top 13%, L-glutamine
WTCS=0, 1/5 negative controls bottom-half). Root cause is signature/
assay data limitations (lost erythroid+HbF axis, 12% landmark overlap),
not the matching algorithm — reported straight per the project ethos.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Build the drug profile dataset (PLAN §6 Week 2):
- week2_curate_drugset.py: 300-drug set (2 ground-truth + 32 related-
mechanism + 26 negative-control + 240 random), restricted to
LINCS-scorable compounds, seed=42
- week2_chembl.py: InChIKey->ChEMBL match (145/300), MoA + targets
- week2_lincs_extract.py: cmapPy-slice both Level-5 GCTX phases to 978
landmark genes, mean-aggregate per drug to one consensus signature
- week2_assemble.py: join into drug_profiles_v1.parquet, Tier B (LINCS
single-source), scored flag per PLAN §6 Week 3 task 2
- docs/data_sources.md: drug set composition + LINCS/ChEMBL provenance
Results (all gitignored data): 300/300 drugs scored, both ground-truth
drugs present (hydroxyurea Phase II = CHEMBL467, L-glutamine Phase I).
Key caveat recorded: only 56/477 (12%) of the disease signature genes
are LINCS landmarks, so Week-3 scoring uses a 30-up/26-down query.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
