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 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.

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

Use
```
data-access.md
```
to download and access latest DrugBank data
Use
```
drug-queries.md
```
to build searchable drug database
Use
```
chemical-analysis.md
```
to find similar compounds
Use
```
targets-pathways.md
```
to identify shared targets
Use
```
interactions.md
```
to check safety of candidate combinations

Polypharmacy Safety Analysis

Use
```
drug-queries.md
```
to look up patient medications
Use
```
interactions.md
```
to check all pairwise interactions
Use
```
interactions.md
```
to classify interaction severity
Use
```
interactions.md
```
to calculate overall risk score
Use
```
targets-pathways.md
```
to understand interaction mechanisms

Drug Repurposing Research

Use
```
targets-pathways.md
```
to find drugs with shared targets
Use
```
chemical-analysis.md
```
to find structurally similar drugs
Use
```
drug-queries.md
```
to extract indication and pharmacology data
Use
```
interactions.md
```
to assess potential combination therapies

Pharmacology Study

Use
```
drug-queries.md
```
to extract drug of interest
Use
```
targets-pathways.md
```
to identify all protein interactions
Use
```
targets-pathways.md
```
to map to biological pathways
Use
```
chemical-analysis.md
```
to predict ADMET properties
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

Create free account at go.drugbank.com
Accept license agreement (free for academic use)
Obtain username and password credentials
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

Credentials: Use environment variables or config files, never hardcode
Versioning: Specify exact database version for reproducibility
Caching: Cache parsed data to avoid re-downloading and re-parsing
Namespaces: Handle XML namespaces properly when parsing
Validation: Validate chemical structures with RDKit before use
Cross-referencing: Use external identifiers (UniProt, PubChem) for integration
Clinical Context: Always consider clinical context when interpreting interaction data
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.

drugbank-database

NPX Install

Tags

SKILL.md Content

DrugBank Database

Overview

Core Capabilities

1. Data Access and Authentication

2. Drug Information Queries

3. Drug-Drug Interactions Analysis

4. Drug Targets and Pathways

5. Chemical Properties and Similarity

Typical Workflows

Drug Discovery Workflow

Polypharmacy Safety Analysis

Drug Repurposing Research

Pharmacology Study

Installation Requirements

Python Packages

Account Setup

Data Version and Reproducibility

Best Practices

Reference Documentation