---
name: alterlab-drugbank
description: Access and analyze drug information from the DrugBank database — drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. Use when working with pharmaceutical data, drug discovery research, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task needing detailed drug and drug-target records from DrugBank. Part of the AlterLab Academic Skills suite.
license: MIT
allowed-tools: Read WebFetch Bash(curl:*) Bash(python:*)
compatibility: Requires a licensed DrugBank account and credentials for data access
metadata:
skill-author: AlterLab
version: "1.0.0"
---
# DrugBank Database
## Overview
DrugBank is a comprehensive bioinformatics and cheminformatics database containing detailed information on drugs and drug targets. This skill enables programmatic access to DrugBank data: thousands of drug entries (FDA-approved small molecules, biotech/biologic drugs, nutraceuticals, and experimental compounds) with 200+ data fields per entry. Exact counts grow with each release — derive them from the version you download rather than quoting a fixed number (the 5.0-era figure was ~9,591 entries).
## Core Capabilities
### 1. Data Access and Authentication
Download and access DrugBank data using Python with proper authentication. The skill provides guidance on:
- Installing and configuring the `drugbank-downloader` package
- Managing credentials securely via environment variables or config files
- Downloading specific or latest database versions
- Opening and parsing XML data efficiently
- Working with cached data to optimize performance
**When to use**: Setting up DrugBank access, downloading database updates, initial project configuration.
**Reference**: See `references/data-access.md` for detailed authentication, download procedures, API access, caching strategies, and troubleshooting.
### 2. Drug Information Queries
Extract comprehensive drug information from the database including identifiers, chemical properties, pharmacology, clinical data, and cross-references to external databases.
**Query capabilities**:
- Search by DrugBank ID, name, CAS number, or keywords
- Extract basic drug information (name, type, description, indication)
- Retrieve chemical properties (SMILES, InChI, molecular formula)
- Get pharmacology data (mechanism of action, pharmacodynamics, ADME)
- Access external identifiers (PubChem, ChEMBL, UniProt, KEGG)
- Build searchable drug datasets and export to DataFrames
- Filter drugs by type (small molecule, biotech, nutraceutical)
**When to use**: Retrieving specific drug information, building drug databases, pharmacology research, literature review, drug profiling.
**Reference**: See `references/drug-queries.md` for XML navigation, query functions, data extraction methods, and performance optimization.
### 3. Drug-Drug Interactions Analysis
Analyze drug-drug interactions (DDIs) including mechanism, clinical significance, and interaction networks for pharmacovigilance and clinical decision support.
**Analysis capabilities**:
- Extract all interactions for specific drugs
- Build bidirectional interaction networks
- Classify interactions by severity and mechanism
- Check interactions between drug pairs
- Identify drugs with most interactions
- Analyze polypharmacy regimens for safety
- Create interaction matrices and network graphs
- Perform community detection in interaction networks
- Calculate interaction risk scores
**When to use**: Polypharmacy safety analysis, clinical decision support, drug interaction prediction, pharmacovigilance research, identifying contraindications.
**Reference**: See `references/interactions.md` for interaction extraction, classification methods, network analysis, and clinical applications.
### 4. Drug Targets and Pathways
Access detailed information about drug-protein interactions including targets, enzymes, transporters, carriers, and biological pathways.
**Target analysis capabilities**:
- Extract drug targets with actions (inhibitor, agonist, antagonist)
- Identify metabolic enzymes (CYP450, Phase II enzymes)
- Analyze transporters (uptake, efflux) for ADME studies
- Map drugs to biological pathways (SMPDB)
- Find drugs targeting specific proteins
- Identify drugs with shared targets for repurposing
- Analyze polypharmacology and off-target effects
- Extract Gene Ontology (GO) terms for targets
- Cross-reference with UniProt for protein data
**When to use**: Mechanism of action studies, drug repurposing research, target identification, pathway analysis, predicting off-target effects, understanding drug metabolism.
**Reference**: See `references/targets-pathways.md` for target extraction, pathway analysis, repurposing strategies, CYP450 profiling, and transporter analysis.
### 5. Chemical Properties and Similarity
Perform structure-based analysis including molecular similarity searches, property calculations, substructure searches, and ADMET predictions.
**Chemical analysis capabilities**:
- Extract chemical structures (SMILES, InChI, molecular formula)
- Calculate physicochemical properties (MW, logP, PSA, H-bonds)
- Apply Lipinski's Rule of Five and Veber's rules
- Calculate Tanimoto similarity between molecules
- Generate molecular fingerprints (Morgan, MACCS, topological)
- Perform substructure searches with SMARTS patterns
- Find structurally similar drugs for repurposing
- Create similarity matrices for drug clustering
- Predict oral absorption and BBB permeability
- Analyze chemical space with PCA and clustering
- Export chemical property databases
**When to use**: Structure-activity relationship (SAR) studies, drug similarity searches, QSAR modeling, drug-likeness assessment, ADMET prediction, chemical space exploration.
**Reference**: See `references/chemical-analysis.md` for structure extraction, similarity calculations, fingerprint generation, ADMET predictions, and chemical space analysis.
## Typical Workflows
### Drug Discovery Workflow
1. Use `data-access.md` to download and access latest DrugBank data
2. Use `drug-queries.md` to build searchable drug database
3. Use `chemical-analysis.md` to find similar compounds
4. Use `targets-pathways.md` to identify shared targets
5. Use `interactions.md` to check safety of candidate combinations
### Polypharmacy Safety Analysis
1. Use `drug-queries.md` to look up patient medications
2. Use `interactions.md` to check all pairwise interactions
3. Use `interactions.md` to classify interaction severity
4. Use `interactions.md` to calculate overall risk score
5. Use `targets-pathways.md` to understand interaction mechanisms
### Drug Repurposing Research
1. Use `targets-pathways.md` to find drugs with shared targets
2. Use `chemical-analysis.md` to find structurally similar drugs
3. Use `drug-queries.md` to extract indication and pharmacology data
4. Use `interactions.md` to assess potential combination therapies
### Pharmacology Study
1. Use `drug-queries.md` to extract drug of interest
2. Use `targets-pathways.md` to identify all protein interactions
3. Use `targets-pathways.md` to map to biological pathways
4. Use `chemical-analysis.md` to predict ADMET properties
5. Use `interactions.md` to identify potential contraindications
## Installation Requirements
### Python Packages
```bash
uv pip install drugbank-downloader # Core access
uv pip install bioversions # Latest version detection
uv pip install lxml # XML parsing optimization
uv pip install pandas # Data manipulation
uv pip install rdkit # Chemical informatics (for similarity)
uv pip install networkx # Network analysis (for interactions)
uv pip install scikit-learn # ML/clustering (for chemical space)
```
### Account Setup
1. Create free account at go.drugbank.com
2. Accept license agreement (free for academic use)
3. Obtain username and password credentials
4. Configure credentials as documented in `references/data-access.md`
## Data Version and Reproducibility
Always specify the DrugBank version for reproducible research:
```python
from drugbank_downloader import download_drugbank
path = download_drugbank(version='5.1.10') # Specify exact version
```
Document the version used in publications and analysis scripts.
## Best Practices
1. **Credentials**: Use environment variables or config files, never hardcode
2. **Versioning**: Specify exact database version for reproducibility
3. **Caching**: Cache parsed data to avoid re-downloading and re-parsing
4. **Namespaces**: Handle XML namespaces properly when parsing
5. **Validation**: Validate chemical structures with RDKit before use
6. **Cross-referencing**: Use external identifiers (UniProt, PubChem) for integration
7. **Clinical Context**: Always consider clinical context when interpreting interaction data
8. **License Compliance**: Ensure proper licensing for your use case
## Reference Documentation
All detailed implementation guidance is organized in modular reference files:
- **references/data-access.md**: Authentication, download, parsing, API access, caching
- **references/drug-queries.md**: XML navigation, query methods, data extraction, indexing
- **references/interactions.md**: DDI extraction, classification, network analysis, safety scoring
- **references/targets-pathways.md**: Target/enzyme/transporter extraction, pathway mapping, repurposing
- **references/chemical-analysis.md**: Structure extraction, similarity, fingerprints, ADMET prediction
Load these references as needed based on your specific analysis requirements.