title: A semantic web ontology for small molecules and their biological targets.
abstract: A wide range of data on sequences, structures, pathways, and networks of genes and gene products is available for hypothesis testing and discovery in biological and biomedical research. However, data describing the physical, chemical, and biological properties of small molecules have not been well-integrated with these resources. Semantically rich representations of chemical data, combined with Semantic Web technologies, have the potential to enable the integration of small molecule and biomolecular data resources, expanding the scope and power of biomedical and pharmacological research. We employed the Semantic Web technologies Resource Description Framework (RDF) and Web Ontology Language (OWL) to generate a Small Molecule Ontology (SMO) that represents concepts and provides unique identifiers for biologically relevant properties of small molecules and their interactions with biomolecules, such as proteins. We instanced SMO using data from three public data sources, i.e., DrugBank, PubChem and UniProt, and converted to RDF triples. Evaluation of SMO by use of predetermined competency questions implemented as SPARQL queries demonstrated that data from chemical and biomolecular data sources were effectively represented and that useful knowledge can be extracted. These results illustrate the potential of Semantic Web technologies in chemical, biological, and pharmacological research and in drug discovery.
authors.affiliation: Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD 4072, Australia.
authors.forename: Jooyoung
authors.initials: J
authors.lastname: Choi
authors.forename: Melissa J
authors.initials: MJ
authors.lastname: Davis
authors.forename: Andrew F
authors.initials: AF
authors.lastname: Newman
authors.forename: Mark A
authors.initials: MA
authors.lastname: Ragan
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D055808:Drug Discovery; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D009323:Natural Language Processing; D012660:Semantics