title: An integrated dataset for in silico drug discovery.
abstract: Drug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.
authors.affiliation: Bioinformatics Support Unit, Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle-upon-Tyne NE1 7RH, UK
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Cockell
authors.forename: Jochen
authors.initials: J
authors.lastname: Weile
authors.forename: Phillip
authors.initials: P
authors.lastname: Lord
authors.forename: Claire
authors.initials: C
authors.lastname: Wipat
authors.forename: Dmytro
authors.initials: D
authors.lastname: Andriychenko
authors.forename: Matthew
authors.initials: M
authors.lastname: Pocock
authors.forename: Darren
authors.initials: D
authors.lastname: Wilkinson
authors.forename: Malcolm
authors.initials: M
authors.lastname: Young
authors.forename: Anil
authors.initials: A
authors.lastname: Wipat
mesh_terms: D002746:Chlorpromazine / Q000032:analysis; D019295:Computational Biology; D019992:Databases as Topic; D055808:Drug Discovery; D012660:Semantics
chemical_list: D002746:Chlorpromazine