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mecddi-query

Name: Mecddi Query
Author: QSong-github

// 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.

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$ git log --oneline --stat

stars:136

forks:4

updated:2026年4月7日 21:05

ファイルエクスプローラー

6 ファイル

SKILL.md

readonly

name

mecddi-query

description

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.

MecDDI Query Skill

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

Input Pattern	Detected As	Match Logic
`D0123`	MecDDI Drug ID	exact on `A_Drug_ID` / `B_Drug_ID`
anything else	free text	substring on `A_Drug_Name` / `B_Drug_Name`

Mechanism Categories (7 files)

Category	Type
Affected Gastrointestinal Absorption	PK
Affected Cellular Transport	PK
Affected Organization Distribution	PK
Affected Intra/Extra-Hepatic Metabolism	PK
Affected Excretion Pathways	PK
Pharmacodynamic Additive Effects	PD
Pharmacodynamic Antagonistic Effects	PD

API

Function	Input	Returns
`load_mecddi(data_dir)`	directory path	list[dict] (all records)
`search(records, entity)`	single entity string	list[dict]
`search_batch(records, entities)`	list of strings	dict[str, list[dict]]
`summarize(hits, entity)`	hits + label	compact 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:

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

related-skills.json

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package.json

"author": "QSong-github"

"repository": "QSong-github/DrugClaw"

GitHub リポジトリを開く Creator のリポジトリを見る

$ install --global

$ download --local

Manusで実行

$ useful --forSOC

ソフトウェア開発者コンピュータ・数学職15-1252L4

name

mecddi-query

description

MecDDI Query Skill

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

Input Pattern	Detected As	Match Logic
`D0123`	MecDDI Drug ID	exact on `A_Drug_ID` / `B_Drug_ID`
anything else	free text	substring on `A_Drug_Name` / `B_Drug_Name`

Mechanism Categories (7 files)

Category	Type
Affected Gastrointestinal Absorption	PK
Affected Cellular Transport	PK
Affected Organization Distribution	PK
Affected Intra/Extra-Hepatic Metabolism	PK
Affected Excretion Pathways	PK
Pharmacodynamic Additive Effects	PD
Pharmacodynamic Antagonistic Effects	PD

API

Function	Input	Returns
`load_mecddi(data_dir)`	directory path	list[dict] (all records)
`search(records, entity)`	single entity string	list[dict]
`search_batch(records, entities)`	list of strings	dict[str, list[dict]]
`summarize(hits, entity)`	hits + label	compact 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:

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

mecddi-query

MecDDI Query Skill

Mechanism Categories (7 files)

API

Data

Usage

このリポジトリの他の Skills

このリポジトリの他の Skills

MecDDI Query Skill

Mechanism Categories (7 files)

API

Data

Usage