Semantic Technologies in Drug Discovery—Potential, Practical, Possibilities

Abstract

Semantic drug discovery has gained significant traction in recent years, with researchers becoming more aware that these technologies enable them to link together and query disparate datasets for information that cannot be extracted from a single dataset. This article provides a comprehensive reference source of the current knowledge available regarding Semantic Web technologies in drug discovery. The main aspects of Semantic Web technologies are explained, detailing the different ways in which they can be used in drug discovery. Over 1000 biomedical ontologies were reviewed as part of the work undertaken for this paper and 34 of the most relevant ontologies in the drug discovery field are categorized and described, followed by details of semantic applications and successes in drug discovery. Some core standards and guidelines have been established for sharing Semantic drug discovery data, both through making well established medical taxonomies available in a Semantic format, and by creating upper-level ontologies and guidelines for creating new ontologies in the biomedical domain. This article concludes that a majority of the prevalent ontologies in drug discovery follow these standards and provides advice for researchers wishing to use Semantic Web technologies in their drug discovery research.