# Alterlab Drugbank > 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. - Skill: `alterlab-ieu/alterlab-drugbank` (Agent Skill, multi-file: 8 files) - Install (CLI): `npx skillmds add alterlab-ieu/alterlab-drugbank` - Raw SKILL.md: https://api.skillmd.com/api/skills/alterlab-ieu/alterlab-drugbank/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - License: MIT - Author: AlterLab-IEU (https://skillmd.com/u/alterlab-ieu) - Updated: 2026-08-19 - Page: https://skillmd.com/skills/alterlab-ieu/alterlab-drugbank --- # 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.