Alterlab Drugbank

---
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 DrugBank account with an approved academic (CC BY-NC 4.0) or commercial license for XML downloads (academic downloads paused as of 2026-09), or a commercial API key for the Clinical API; drugbank-downloader >= 0.2 (Python >= 3.10)
metadata:
    skill-author: AlterLab
    version: "1.1.0"
    last_updated: "2026-09-23"
---

# 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).

**Access status (checked 2026-09-23).** The current downloadable release is **5.1.22**
(2026-06-27). DrugBank's releases page states that *all academic dataset downloads are
temporarily paused* while it changes how data are distributed, and every download link
(including the CC0 "Open Data" vocabulary and structures) shows "Temporarily
unavailable" — scripted downloads return HTTP 403. Until access resumes, work from a
copy you already downloaded (pin and cite its version), check
https://go.drugbank.com/releases/latest, or route the question to an open source listed
under "Does NOT Trigger". Academic data are licensed CC BY-NC 4.0; commercial use and
the Clinical API (`https://api.drugbank.com/v1/`, API-key header) need a paid license.

## When to Use This Skill

- Parsing a DrugBank XML release you have access to (drug records, targets, enzymes, transporters, pathways)
- Drug–drug interaction extraction and polypharmacy screening from DrugBank data
- Drug–target mapping and repurposing via shared targets or structural similarity
- Pulling DrugBank cross-references (UniProt, ChEMBL, PubChem, KEGG) for integration

### Does NOT Trigger

| Scenario | Use Instead |
|----------|-------------|
| FDA drug labels, FAERS adverse events, recalls, approvals (open data) | `alterlab-fda` |
| Compound bioactivity (IC50/Ki), mechanisms, and indications from open data | `alterlab-chembl` |
| Genotype-guided dosing / pharmacogene–drug pairs (CPIC, DPWG) | `alterlab-clinpgx` |
| KEGG DRUG entries, drug–pathway maps, KEGG drug–drug interactions | `alterlab-kegg` |
| Compound identifiers and properties by name/CID/SMILES | `alterlab-pubchem` |

## 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 an account at go.drugbank.com
2. Apply for the Academic License (free for eligible, non-commercial academic research;
   data under CC BY-NC 4.0) or obtain a commercial license
3. Use your account email and password as download credentials (academic downloads
   were paused as of 2026-09 — see Access status above)
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='5.1.22')  # Pin an exact version (5.1.22 = 2026-06-27)
```

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.