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drugbank-database

Query DrugBank drug properties and analyze interactions using XML data.

1|Updated Jan 14, 2026
Search Tags:#drug discovery#drug interactions#cheminformatics#pharmacology#drugbank#drug targets
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill drugbank-database-sologa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drugbank-database
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/drugbank-database
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill drugbank-database-sologa

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 comprehensive DrugBank database, enabling detailed analysis of drugs, their properties, interactions, and targets, eliminating the need for manual data retrieval and complex parsing.

Core Features & Use Cases

  • Comprehensive Data Access: Download and query DrugBank's extensive collection of drug and target information.
  • Interaction Analysis: Analyze drug-drug interactions, identify potential risks, and build interaction networks.
  • Target & Pathway Exploration: Investigate drug targets, metabolic enzymes, transporters, and biological pathways.
  • Chemical Analysis: Perform structure-based analysis, similarity searches, and ADMET predictions.
  • Use Case: A researcher can use this Skill to quickly find all known interactions for a given drug, identify its primary protein targets, and check for structural similarity to other known compounds, accelerating drug discovery and safety assessments.

Quick Start

Use the drugbank-database skill to get the basic information for drug 'DB00001'.

Frequently Asked Questions about drugbank-database

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

FAQPage Schema
How do I programmatically query drug properties and targets from the DrugBank XML database?▼

To query drug properties and targets from DrugBank XML, use the drugbank-downloader package to download and parse the database, then leverage pandas and lxml for structured data extraction and analysis.

Can I analyze drug-drug interactions and build interaction networks using Python?▼

Yes, you can analyze drug-drug interactions and build networks in Python by parsing DrugBank data and applying the networkx library to map pharmacological relationships and identify potential risks.

Do I need the drugbank-downloader package to access drug target and pathway information?▼

Yes, the drugbank-downloader package is required to download DrugBank data and parse XML files for extracting drug targets, metabolic enzymes, transporters, and biological pathway information.

What's the best way to perform chemical structure-based analysis and similarity searches on drug compounds?▼

To perform chemical structure-based analysis and similarity searches on drug compounds, combine DrugBank data with the rdkit library to compute structural similarities and generate ADMET predictions.

How does cheminformatics integrate with machine learning for drug discovery using DrugBank data?▼

Cheminformatics integrates with machine learning for drug discovery by using rdkit to extract molecular features from DrugBank compounds, then applying scikit-learn models to predict drug properties and identify candidates.

Are there limitations when using lxml and pandas for parsing large-scale pharmacology datasets?▼

Limitations of using lxml and pandas for large-scale pharmacology datasets include potential high memory consumption during XML parsing, requiring careful management of complex drug interaction and chemical structure data trees.

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