菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| 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.pyQuery 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.