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Mecddi

Skill BioTender-max/awesome-bio-agent-skills/skills/drugclaw/mecddi

Query the MecDDI mechanism-based drug-drug interaction database. Use whenever the user asks about drug-drug interactions, DDI mechanisms (PK/PD), enzyme or transporter-mediated interactions, or wants to look up interacting drug pairs by drug name or MecDDI drug ID. Trigger on keywords like DDI, drug interaction, MecDDI, mechanism-based interaction, pharmacokinetic interaction, pharmacodynamic interaction, or any query involving two drugs that may interact.From its SKILL.md

Install
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill mecddi

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SKILL.md

2.2 KB, 528 tokens by cl100k_base, as published. Nobody here has run it

MecDDI Query Skill

Search MecDDI drug-drug interaction records by drug name or drug ID. Auto-detects input type:

Input PatternDetected AsMatch Logic
D0123MecDDI Drug IDexact on A_Drug_ID / B_Drug_ID
anything elsefree textsubstring on A_Drug_Name / B_Drug_Name

Mechanism Categories (7 files)

CategoryType
Affected Gastrointestinal AbsorptionPK
Affected Cellular TransportPK
Affected Organization DistributionPK
Affected Intra/Extra-Hepatic MetabolismPK
Affected Excretion PathwaysPK
Pharmacodynamic Additive EffectsPD
Pharmacodynamic Antagonistic EffectsPD

API

FunctionInputReturns
load_mecddi(data_dir)directory pathlist[dict] (all records)
search(records, entity)single entity stringlist[dict]
search_batch(records, entities)list of stringsdict[str, list[dict]]
summarize(hits, entity)hits + labelcompact text for LLM
to_json(hits)list[dict]JSON string

Data

  • Source: 7 TSV files downloaded from https://mecddi.idrblab.net/download
  • Path: DATA_DIR variable in 19_MecDDI.py (default: resources_metadata/ddi/MecDDI)
  • Columns: A_Drug_ID, A_Drug_Name, B_Drug_ID, B_Drug_Name, Mechanism_Category

Usage

See if __name__ == "__main__" block in 19_MecDDI.py for runnable examples covering:

  1. Single drug name → search(data, "Atropine")
  2. Single drug ID → search(data, "D0123")
  3. Batch query → search_batch(data, ["Meclizine", "Isocarboxazid", "D0853"])
  4. JSON output → to_json(hits)
  5. LLM-friendly summary → summarize(hits, entity)

What ships with it: 5 files

9.2 KB alongside SKILL.md, 4 of them executable

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