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Query the Broad Institute Drug Repurposing Hub (~6,800 compounds). Look up drugs by name, gene target, MOA, disease area, Broad ID, or InChIKey. Returns clinical phase, mechanism of action, targets, disease area, indication, and chemical identifiers.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Query the DrugComb drug combination database for cancer cell-line synergy and sensitivity data. Use whenever the user asks about drug combinations, synergy scores (ZIP/Bliss/Loewe/HSA), combination sensitivity (CSS), or wants to look up how two drugs interact in a specific cancer cell line.
Query canonical DrugCombDB combination records. Use when the user asks about drug pairs, synergy values, or cell-line-specific combination evidence.
Query or inspect the FDA Orange Book - FDA-Approved Drug Products Listing resource for drug-centric tasks with emphasis on drug knowledgebase Use whenever Codex needs the calling pattern, downloadable entrypoint, or example query flow from this skill example script.
Query the RepurposeDrugs single-agent drug repurposing database. Use whenever the user asks about drug-disease repurposing associations, clinical trial phases for repurposed drugs, or wants to look up any entity (drug name, disease name, NCT ID) in RepurposeDrugs.
Query the Gene-Drug Knowledge Database (GDKD) for variant-specific gene–drug associations in oncology. Use when the user asks about cancer genomic biomarkers, drug sensitivity/resistance by gene or variant, targetable mutations, or clinical evidence for cancer therapeutics.
Query canonical TarKG drug-target triplets. Use when the user asks about drug-target interactions, relation labels, disease/pathway context, or quick lookups for drugs/targets in TarKG.
| name | Drug-Repurposing-Hub |
| description | Query the Broad Institute Drug Repurposing Hub (~6,800 compounds). Look up drugs by name, gene target, MOA, disease area, Broad ID, or InChIKey. Returns clinical phase, mechanism of action, targets, disease area, indication, and chemical identifiers. |
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 |
| 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) |
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"])
repo-drug-annotation-20200324.txt — tab-delimited, !-prefixed comment lines
pert_iname, clinical_phase, moa, target, disease_area, indicationrepo-sample-annotation-20240610.txt — tab-delimited, !-prefixed comment lines
broad_id, pert_iname, InChIKey, pubchem_cid, smiles, vendor, purity, etc.pert_iname; first sample with non-empty InChIKey is kept per drugDATA_DIR variable in 29_Drug_Repurposing_Hub.py