Can this skill help with drug repurposing research?

Absolutely. It enables you to find drugs with shared targets or high structural similarity, extract indication data, and map compounds to biological pathways, which are essential for repurposing studies.

Does it support RDKit for chemical informatics?

Yes, the skill includes patterns for using RDKit to calculate molecular properties, generate fingerprints, perform substructure searches, and conduct Tanimoto similarity calculations.

How does it handle drug-drug interactions?

It provides capabilities to extract interaction mechanisms, classify them by clinical significance, and build bidirectional interaction networks for pharmacovigilance.

What data can I access using this DrugBank skill?

You can access detailed properties for over 9,500 drugs, including chemical structures (SMILES/InChI), drug-drug interactions, protein targets, metabolic enzymes, biological pathways, and pharmacodynamics.

Do I need a DrugBank account to use this skill?

Yes, you need to create a free account at go.drugbank.com to download the database. The skill provides guidance on configuring your credentials securely via environment variables.

DrugBank Database Integration

Name: DrugBank Database Integration
Author: Activer007

byActiver007

Ciencia de Datos y ML

Accesses and analyzes comprehensive pharmaceutical data from DrugBank for drug discovery and pharmacological research.

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The DrugBank Database skill provides Claude with programmatic access to a massive bioinformatics and cheminformatics repository containing detailed information on over 9,500 drugs and their biological targets. It streamlines workflows for drug discovery, pharmacology studies, and clinical safety analysis by enabling automated data retrieval, interaction checks, and chemical similarity searches. This skill is essential for researchers and developers working on pharmaceutical informatics, allowing them to cross-reference compounds, analyze drug-protein pathways, and predict ADMET properties using standardized Python libraries and structured XML parsing.

Características Principales

Integration with bioinformatics tools like RDKit, pandas, and drugbank-downloader
Comprehensive drug-drug interaction (DDI) analysis and risk scoring
Programmatic access to 9,500+ drug entries and 200+ data fields per entry
Detailed target identification, enzyme profiling, and pathway mapping
Structure-based analysis including SMILES, fingerprints, and Tanimoto similarity
0 GitHub stars

Casos de Uso

Automating drug-drug interaction checks for polypharmacy safety analysis
Building curated pharmaceutical datasets for machine learning model training
Identifying drug repurposing candidates through target and structural similarity

Acerca de

Características Principales

Integration with bioinformatics tools like RDKit, pandas, and drugbank-downloader
Comprehensive drug-drug interaction (DDI) analysis and risk scoring
Programmatic access to 9,500+ drug entries and 200+ data fields per entry
Detailed target identification, enzyme profiling, and pathway mapping
Structure-based analysis including SMILES, fingerprints, and Tanimoto similarity
0 GitHub stars

Casos de Uso

Automating drug-drug interaction checks for polypharmacy safety analysis
Building curated pharmaceutical datasets for machine learning model training
Identifying drug repurposing candidates through target and structural similarity