|Title||Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||David Wild, Ying Ding, Lee Harland, Amit Sheth, Eric Gifford, Michael Lajiness|
|Keywords||chemogenomics, domain specific semantics, domain-specific semantics, drug discovery, drug repurposing, rdf, semantic web for drug discovery, semantic web for life science, semantics for chemical biology, semantics for personalized medicine, sparql|
Systems chemical biology, the integration of chemistry, biology and computation to generate understanding about the way small molecules affect biological systems as a whole, as well as related fields such as chemogenomics, are central to emerging new paradigms of drug discovery such as drug repurposing and personalized medicine. Recent Semantic Web technologies such as RDF and SPARQL are technical enablers of systems chemical biology, facilitating the deployment of advanced algorithms for searching and mining large integrated datasets. In this paper, we aim to demonstrate how these technologies together can change the way that drug discovery is accomplished.
|Full Text|| |
David J. Wild, Ying Ding, Amit P. Sheth, Lee Harland, Eric M. Gifford, Michael S. Lajiness, Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research, Drug Discovery Today, 29 December 2011, ISSN 1359-6446, 10.1016/j.drudis.2011.12.019. PMID: 22222943