Junior B. 07705a5884 GPU Phase 1: co-fold cofactors/metals (the binding-mode determinants)
Add metal/cofactor handling to the Boltz-2 YAML as CCD ligand entries -
the modes classical docking couldn't model:
- HDAC2 + catalytic Zn (vorinostat chelates it)
- PKR + FBP + Mg (allosteric activator + metal)
- hemoglobin + heme
Same cofactors present when co-folding negatives into a target (fair test).

build_boltz_yaml() gains a cofactor_ccds arg (emits `ligand: {ccd: ...}`
entries); TARGETS carries per-target cofactors; cofold()/main() thread them
through. Verified locally: YAML builds correctly with Zn / FBP+Mg.

Honest limitation noted: Hb's voxelotor site is at the tetramer centre and
covalent (Schiff base), so single-chain+heme only approximates it - HDAC2
(Zn) and PKR (cofactor) are the real co-folding tests. Ready for
`modal run gpu/modal_app.py`.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-24 17:16:06 +02:00

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 3.113.13 (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:

  1. docs/recovery_test_report.md — the 2-page write-up
  2. data/results/ranked_candidates_v1.csv — the ranked drug list
  3. 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.

Description
Drug Repurposing Tool
Readme 377 KiB
Languages
Python 93.8%
Jupyter Notebook 6.2%