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drugbank-database

Access DrugBank XML data for drug interactions, targets, and chemical properties.

Updated Mar 10, 2026
Search Tags:#drug discovery#drug interactions#pharmacology#drugbank#drug targets#chemical analysis
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill drugbank-database-felixboehm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drugbank-database
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/drugbank-database
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill drugbank-database-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires drugbank-downloader, bioversions, lxml, pandas, rdkit, networkx, scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides programmatic access to the vast DrugBank database, enabling detailed analysis of drug properties, interactions, and targets, which is crucial for pharmaceutical research and drug discovery.

Core Features & Use Cases

  • Data Access: Download and parse the complete DrugBank XML database.
  • Drug Queries: Retrieve detailed information on specific drugs, including chemical properties, pharmacology, and external identifiers.
  • Interaction Analysis: Analyze drug-drug interactions, identify potential contraindications, and assess polypharmacy risks.
  • Target & Pathway Mapping: Explore drug targets, metabolic enzymes, transporters, and associated biological pathways.
  • Chemical Similarity: Perform structure-based analysis, calculate molecular similarity, and predict ADMET properties.
  • Use Case: A researcher needs to identify all FDA-approved drugs that target a specific protein kinase and have a high risk of metabolic drug-drug interactions. This Skill can efficiently retrieve this information by querying DrugBank.

Quick Start

Use the drugbank-database skill to get the chemical properties for the drug with DrugBank ID 'DB00001'.

Frequently Asked Questions about drugbank-database

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I retrieve chemical properties and pharmacology data for a specific drug from DrugBank?▼

To retrieve chemical properties and pharmacology data, use this Skill to programmatically access the DrugBank database and parse the XML structure. It extracts detailed drug information, including external identifiers and pharmacological profiles, for any specified DrugBank ID.

Can I analyze drug-drug interactions and identify polypharmacy risks using Python?▼

Yes, you can analyze drug-drug interactions and assess polypharmacy risks using this Skill. It parses DrugBank data to identify potential contraindications and interaction profiles, facilitating comprehensive polypharmacy risk assessment directly in Python.

How does XML parsing work for extracting drug targets and biological pathway mappings?▼

XML parsing extracts drug targets and biological pathway mappings by downloading the complete DrugBank XML database and navigating its tree structure. The Skill uses lxml to isolate metabolic enzymes, transporters, and associated pathways for detailed mapping.

Do I need the drugbank-downloader package to calculate molecular similarity and predict ADMET properties?▼

Yes, the drugbank-downloader package is required to access the raw DrugBank XML data needed for analysis. Once downloaded, the Skill leverages rdkit to calculate molecular similarity and predict ADMET properties based on structural data.

What is the best way to identify FDA-approved drugs targeting a specific protein kinase?▼

The best way to identify FDA-approved drugs for a specific protein kinase is to query the DrugBank database using this Skill. It filters approved drugs by target identification and pathway mapping, efficiently retrieving compounds that interact with the specified kinase.

Are there limitations when using pandas and scikit-learn for large-scale drug discovery analysis?▼

Limitations may arise from memory constraints when loading the complete DrugBank XML database into pandas for large-scale drug discovery. While scikit-learn enables ADMET prediction, processing the entire dataset requires sufficient computational resources to avoid performance bottlenecks.

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