# Bioconductor Drugtargetinteractions

Provides utilities for identifying drug-target interactions for sets of small molecule...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Data and ML
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-drugtargetinteractions -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-drugtargetinteractions -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-drugtargetinteractions -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-drugtargetinteractions -g -a gemini-cli -y`

## Description

Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# drugTargetInteractions

## Workflows

### Standard Workflow

Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.

```r
library(drugTargetInteractions)
chembldb <- system.file("extdata", "chembl_sample.db", package="drugTargetInteractions")
resultsPath <- system.file("extdata", "results", package="drugTargetInteractions")
config <- genConfig(chemblDbPath=chembldb, resultsPath=resultsPath)
downloadUniChem(config=config)
cmpIdMapping(config=config)
queryBy <- list(molType="cmp", idType="chembl_id", ids=c("CHEMBL17", "CHEMBL19"))
qresult1 <- drugTargetAnnot(queryBy, config=config)
```
Input: A list containing query molecule/protein IDs and a configuration object. Output: A data.frame of drug-target annotations.

## When to Use
- Identify drug-target interactions for sets of small molecules or gene/protein identifiers using a local SQLite instance of the ChEMBL database.
- Retrieve UniProt IDs for Ensembl gene IDs using sequence similarity nearest neighbors (SSNNs) via `getUniprotIDs` or paralogs via `getParalogs`.
- Query bioactivity data for compounds or proteins using `drugTargetBioactivity`.

## When NOT to Use
- For high-throughput de novo molecular docking or 3D structure-based drug design, use packages like `Rcpi` or external tools.
- For querying online databases directly without a local SQLite copy of ChEMBL, use direct web API clients.

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/proteomics/drugtargetinteractions/SKILL.md