Drug Repurposing Hub Query Skill

Search the Broad Institute Drug Repurposing Hub by any entity. Auto-detects input type by pattern:

Input Pattern	Detected As	Match Logic
BRD-A12345678	Broad compound ID	prefix on broad_id
ABCDEFGHIJKLMN-OPQRSTUVWX-Y	InChIKey	exact on InChIKey
EGFR, BRAF, TOP1	Gene / target	exact token in target (pipe-separated)
anything else	free text	substring on pert_iname, moa, indication, disease_area

API

Function	Input	Returns
load_drugs(path)	drug TSV path	list[dict]
load_samples(path)	sample TSV path	list[dict]
load_merged()	—	list[dict] (drugs + chemical IDs from samples)
search(entity)	single entity string	list[dict]
search_batch(entities)	list of entity strings	dict[str, list[dict]]
summarize(hits, entity)	hits + label	compact LLM-readable text
to_json(hits)	list[dict]	list[dict] (JSON-serialisable)

Usage

See if __name__ == "__main__" block in 29_Drug_Repurposing_Hub.py for runnable examples covering: drug name, gene target, MOA keyword, disease area, batch search, and JSON output.

from importlib.machinery import SourceFileLoader
hub = SourceFileLoader("hub", "29_Drug_Repurposing_Hub.py").load_module()

# Single drug lookup
hits = hub.search("imatinib")
print(hub.summarize(hits, "imatinib"))

# Target-based search
hits = hub.search("EGFR")

# Batch
results = hub.search_batch(["metformin", "aspirin", "BRAF"])

Data

• Source: Broad Institute Drug Repurposing Hub (https://repo-hub.broadinstitute.org/repurposing)
• Drug file: repo-drug-annotation-20200324.txt — tab-delimited, !-prefixed comment lines
  • Columns: pert_iname, clinical_phase, moa, target, disease_area, indication
• Sample file: repo-sample-annotation-20240610.txt — tab-delimited, !-prefixed comment lines
  • Columns include: broad_id, pert_iname, InChIKey, pubchem_cid, smiles, vendor, purity, etc.
• Merge: on pert_iname; first sample with non-empty InChIKey is kept per drug
• Path: DATA_DIR variable in 29_Drug_Repurposing_Hub.py
• Citation: Corsello SM et al. Nature Medicine 23, 405–408 (2017). doi:10.1038/nm.4306