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This skill enables programmatic access to DrugBank data including ~9,591 drug entries (2,037 FDA-approved small molecules, 241 biotech drugs, 96 nutraceuticals, and 6,000+ experimental compounds) with 200+ data fields per entry.\n\n## Core Capabilities\n\n### 1. Data Access and Authentication\n\nDownload and access DrugBank data using Python with proper authentication. The skill provides guidance on:\n\n- Installing and configuring the `drugbank-downloader` package\n- Managing credentials securely via environment variables or config files\n- Downloading specific or latest database versions\n- Opening and parsing XML data efficiently\n- Working with cached data to optimize performance\n\n**When to use**: Setting up DrugBank access, downloading database updates, initial project configuration.\n\n**Reference**: See `references\u002Fdata-access.md` for detailed authentication, download procedures, API access, caching strategies, and troubleshooting.\n\n### 2. Drug Information Queries\n\nExtract comprehensive drug information from the database including identifiers, chemical properties, pharmacology, clinical data, and cross-references to external databases.\n\n**Query capabilities**:\n- Search by DrugBank ID, name, CAS number, or keywords\n- Extract basic drug information (name, type, description, indication)\n- Retrieve chemical properties (SMILES, InChI, molecular formula)\n- Get pharmacology data (mechanism of action, pharmacodynamics, ADME)\n- Access external identifiers (PubChem, ChEMBL, UniProt, KEGG)\n- Build searchable drug datasets and export to DataFrames\n- Filter drugs by type (small molecule, biotech, nutraceutical)\n\n**When to use**: Retrieving specific drug information, building drug databases, pharmacology research, literature review, drug profiling.\n\n**Reference**: See `references\u002Fdrug-queries.md` for XML navigation, query functions, data extraction methods, and performance optimization.\n\n### 3. Drug-Drug Interactions Analysis\n\nAnalyze drug-drug interactions (DDIs) including mechanism, clinical significance, and interaction networks for pharmacovigilance and clinical decision support.\n\n**Analysis capabilities**:\n- Extract all interactions for specific drugs\n- Build bidirectional interaction networks\n- Classify interactions by severity and mechanism\n- Check interactions between drug pairs\n- Identify drugs with most interactions\n- Analyze polypharmacy regimens for safety\n- Create interaction matrices and network graphs\n- Perform community detection in interaction networks\n- Calculate interaction risk scores\n\n**When to use**: Polypharmacy safety analysis, clinical decision support, drug interaction prediction, pharmacovigilance research, identifying contraindications.\n\n**Reference**: See `references\u002Finteractions.md` for interaction extraction, classification methods, network analysis, and clinical applications.\n\n### 4. Drug Targets and Pathways\n\nAccess detailed information about drug-protein interactions including targets, enzymes, transporters, carriers, and biological pathways.\n\n**Target analysis capabilities**:\n- Extract drug targets with actions (inhibitor, agonist, antagonist)\n- Identify metabolic enzymes (CYP450, Phase II enzymes)\n- Analyze transporters (uptake, efflux) for ADME studies\n- Map drugs to biological pathways (SMPDB)\n- Find drugs targeting specific proteins\n- Identify drugs with shared targets for repurposing\n- Analyze polypharmacology and off-target effects\n- Extract Gene Ontology (GO) terms for targets\n- Cross-reference with UniProt for protein data\n\n**When to use**: Mechanism of action studies, drug repurposing research, target identification, pathway analysis, predicting off-target effects, understanding drug metabolism.\n\n**Reference**: See `references\u002Ftargets-pathways.md` for target extraction, pathway analysis, repurposing strategies, CYP450 profiling, and transporter analysis.\n\n### 5. Chemical Properties and Similarity\n\nPerform structure-based analysis including molecular similarity searches, property calculations, substructure searches, and ADMET predictions.\n\n**Chemical analysis capabilities**:\n- Extract chemical structures (SMILES, InChI, molecular formula)\n- Calculate physicochemical properties (MW, logP, PSA, H-bonds)\n- Apply Lipinski's Rule of Five and Veber's rules\n- Calculate Tanimoto similarity between molecules\n- Generate molecular fingerprints (Morgan, MACCS, topological)\n- Perform substructure searches with SMARTS patterns\n- Find structurally similar drugs for repurposing\n- Create similarity matrices for drug clustering\n- Predict oral absorption and BBB permeability\n- Analyze chemical space with PCA and clustering\n- Export chemical property databases\n\n**When to use**: Structure-activity relationship (SAR) studies, drug similarity searches, QSAR modeling, drug-likeness assessment, ADMET prediction, chemical space exploration.\n\n**Reference**: See `references\u002Fchemical-analysis.md` for structure extraction, similarity calculations, fingerprint generation, ADMET predictions, and chemical space analysis.\n\n## Typical Workflows\n\n### Drug Discovery Workflow\n1. Use `data-access.md` to download and access latest DrugBank data\n2. Use `drug-queries.md` to build searchable drug database\n3. Use `chemical-analysis.md` to find similar compounds\n4. Use `targets-pathways.md` to identify shared targets\n5. Use `interactions.md` to check safety of candidate combinations\n\n### Polypharmacy Safety Analysis\n1. Use `drug-queries.md` to look up patient medications\n2. Use `interactions.md` to check all pairwise interactions\n3. Use `interactions.md` to classify interaction severity\n4. Use `interactions.md` to calculate overall risk score\n5. Use `targets-pathways.md` to understand interaction mechanisms\n\n### Drug Repurposing Research\n1. Use `targets-pathways.md` to find drugs with shared targets\n2. Use `chemical-analysis.md` to find structurally similar drugs\n3. Use `drug-queries.md` to extract indication and pharmacology data\n4. Use `interactions.md` to assess potential combination therapies\n\n### Pharmacology Study\n1. Use `drug-queries.md` to extract drug of interest\n2. Use `targets-pathways.md` to identify all protein interactions\n3. Use `targets-pathways.md` to map to biological pathways\n4. Use `chemical-analysis.md` to predict ADMET properties\n5. Use `interactions.md` to identify potential contraindications\n\n## Installation Requirements\n\n### Python Packages\n```bash\nuv pip install drugbank-downloader  # Core access\nuv pip install bioversions          # Latest version detection\nuv pip install lxml                 # XML parsing optimization\nuv pip install pandas               # Data manipulation\nuv pip install rdkit                # Chemical informatics (for similarity)\nuv pip install networkx             # Network analysis (for interactions)\nuv pip install scikit-learn         # ML\u002Fclustering (for chemical space)\n```\n\n### Account Setup\n1. Create free account at go.drugbank.com\n2. Accept license agreement (free for academic use)\n3. Obtain username and password credentials\n4. Configure credentials as documented in `references\u002Fdata-access.md`\n\n## Data Version and Reproducibility\n\nAlways specify the DrugBank version for reproducible research:\n\n```python\nfrom drugbank_downloader import download_drugbank\npath = download_drugbank(version='5.1.10')  # Specify exact version\n```\n\nDocument the version used in publications and analysis scripts.\n\n## Best Practices\n\n1. **Credentials**: Use environment variables or config files, never hardcode\n2. **Versioning**: Specify exact database version for reproducibility\n3. **Caching**: Cache parsed data to avoid re-downloading and re-parsing\n4. **Namespaces**: Handle XML namespaces properly when parsing\n5. **Validation**: Validate chemical structures with RDKit before use\n6. **Cross-referencing**: Use external identifiers (UniProt, PubChem) for integration\n7. **Clinical Context**: Always consider clinical context when interpreting interaction data\n8. **License Compliance**: Ensure proper licensing for your use case\n\n## Reference Documentation\n\nAll detailed implementation guidance is organized in modular reference files:\n\n- **references\u002Fdata-access.md**: Authentication, download, parsing, API access, caching\n- **references\u002Fdrug-queries.md**: XML navigation, query methods, data extraction, indexing\n- **references\u002Finteractions.md**: DDI extraction, classification, network analysis, safety scoring\n- **references\u002Ftargets-pathways.md**: Target\u002Fenzyme\u002Ftransporter extraction, pathway mapping, repurposing\n- **references\u002Fchemical-analysis.md**: Structure extraction, similarity, fingerprints, ADMET prediction\n\nLoad these references as needed based on your specific analysis 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