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>
Reverso MVP — Sickle Cell Repurposing Pipeline
A minimum viable drug repurposing pipeline for sickle cell disease: build a disease signature from public transcriptomic data, build drug profiles for ~300 small molecules, and rank them by CMap-style connectivity scoring. Validated by a recovery test — do the two known sickle cell drugs (hydroxyurea, L-glutamine) rank near the top?
See PLAN.md for the full specification, locked decisions, and week-by-week build plan.
Quickstart
# Requires Python >=3.11,<3.13 (see note below)
pip install -e . # or: pip install -e ".[dev]" for test/lint tooling
pytest # run unit tests
Python version note: use Python 3.11–3.13 (
python3.13 -m venv .venv). Python 3.14 is not yet supported by all pipeline dependencies (pydeseq2,cmapPy).
Project layout
data/ raw (downloaded, never edited) / processed / results — gitignored
notebooks/ 01..05, run end-to-end in order
src/ identifiers, disease, drugs, scoring, provenance
tests/ scoring unit tests
docs/ recovery_test_report.md, data_sources.md, known_limitations.md
The deliverable
When complete, the artifact to share is three files:
docs/recovery_test_report.md— the 2-page write-updata/results/ranked_candidates_v1.csv— the ranked drug list- The signature + drug profile files with provenance
Pipeline
| Notebook | Stage | Output |
|---|---|---|
01_setup_identifiers.ipynb |
Pin disease/gene IDs | data/processed/identifiers.json |
02_disease_signature.ipynb |
GEO + differential expression | sickle_cell_signature_v1.json |
03_drug_profiles.ipynb |
ChEMBL + LINCS | drug_profiles_v1.parquet |
04_connectivity_scoring.ipynb |
CMap scoring | ranked_candidates_v1.csv |
05_recovery_test.ipynb |
Validation | docs/recovery_test_report.md |
Every persisted artifact carries a confidence tier (A/B/C) and provenance. See PLAN.md §3.