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drugtargetinteractions

Skill file skills/proteomics/drugtargetinteractions/SKILL.md in bioMate-AI/biomate-bioconductor-kb. Read the publisher’s upstream source.

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bioconductor-drugtargetinteractions

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Use bioconductor-drugtargetinteractions to prepare a drug-target annotation script for [compound IDs and identifier type] using [local full ChEMBL SQLite database] and [results directory with prepared UniChem mappings].

Required inputs

  • [compound IDs and identifier type]
  • [local full ChEMBL SQLite database]
  • [results directory with prepared UniChem mappings]

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SKILL.md

name: bioconductor-drugtargetinteractionsdescription: 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.when_to_use: Use when: Drug-Target Interactions; Metabolomics data analysis (drugTargetInteractions); Proteomics quantification (drugTargetInteractions). Not for: Requires R ≥ 4.1 and Bioconductor ≥ 3.16user-invocable: false

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.

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.

Data Requirements

  • A local SQLite instance of the ChEMBL database (e.g., chembl_sample.db for testing).
  • Query identifiers formatted as a list with molType (e.g., "cmp", "protein", "gene"), idType (e.g., "chembl_id", "UniProt_ID", "ensembl_gene_id"), and a character vector of ids.

Key Parameters

  • chemblDbPath (NULL): Path to the local ChEMBL SQLite database file passed to genConfig.
  • resultsPath (NULL): Path to the directory where results are stored, passed to genConfig.
  • taxId (9606): Taxonomy ID for organism selection in getUniprotIDs.
  • kt ("ENSEMBL"): Keytype of the input keys in getUniprotIDs.
  • seq_cluster ("UNIREF90"): Sequence similarity level ("UNIREF100", "UNIREF90", "UNIREF50") in getUniprotIDs.
  • id_mapping (c(chembl="chembl_id", pubchem="PubChem_ID", uniprot="UniProt_ID", drugbank="DrugBank_ID")): Mapping vector used in getDrugTarget.

Best Practices

  • Use genConfig to set up paths to the local ChEMBL SQLite database and output directories before running queries.
  • Run downloadUniChem and cmpIdMapping to download and prepare compound identifier mapping tables.
  • Use getParalogs instead of getUniprotIDs when faster execution and wider evolutionary distances are preferred.
  • Combine ID mapping and drug-target annotation steps using the meta function runDrugTarget_Annot_Bioassay.

Common Pitfalls

  • Using the toy database chembl_sample.db for real analyses: replace the path in genConfig with the path to a full downloaded ChEMBL SQLite database.
  • Slow execution of getUniprotIDs: set chunksize=1 only when tracking query Ensembl gene ID information is strictly required, or use getParalogs as a faster alternative.
  • Missing UniProt to Ensembl mappings in final tables: use tapply to collapse Ensembl IDs when mapping back to UniProt IDs.

Alternatives

  • biomaRt for general gene/protein annotation and paralog retrieval.
  • UniProt.ws for direct web-based UniProt ID mapping.
  • ensembldb for local genomic and protein feature mapping.

Citations

  • Wu et al. 2006, Nucleic Acids Res (The Universal Protein Resource)
  • Gaulton et al. 2012, Nucleic Acids Res (ChEMBL database)
  • Bento et al. 2014, Nucleic Acids Res (ChEMBL bioactivity database update)

References