Junior B. 47b0094079 Week 2: 300-drug profiles with LINCS signatures + ChEMBL
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>
2026-06-23 22:25:00 +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 353 KiB
Languages
Python 93.8%
Jupyter Notebook 6.2%