Junior B.
4022c0cb94
Flesh out the Modal app into a runnable Phase-1 positive-control test and reconcile it with the plan: - cofold() GPU fn: build Boltz-2 YAML (protein+ligand+affinity), run `boltz predict --use_msa_server --cache /weights/boltz`, parse affinity JSON + predicted pose; weights persist via Volume. - Local helpers (CPU, import-tested against our PDBs): binding_chain_sequence (gemmi -- correctly picks the binding chain, e.g. alpha-globin for 5E83), pubchem_smiles, build_boltz_yaml, fetch_pdb (RCSB). - main(): fan out cofold.starmap over 3 targets x (known binder + 2 negatives); tabulate; PASS if known binder has top P(binder) for its target. Alignment fixes: - Rank by P(binder) (higher=better), NOT raw affinity_pred_value whose sign (~log IC50) is version-dependent -- avoids a backwards positive-control test. - gpu_plan.md Phase 1 updated to affinity/P(binder) ranking; pose-RMSD noted as a later refinement (needs receptor superposition). Local half verified (sequence/SMILES/YAML); cofold() needs a live `modal run` (account + `modal token new`) to validate end-to-end. 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.