An integrated dataset for in silico drug discovery

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doi doi:10.2390/biecoll-jib-2010-116
submission February 19, 2010
published March 25, 2010
NCBI PubMed PubMed ID 20375448

Simon J Cockell, Jochen Weile, Phillip Lord, Claire Wipat, Dmytro Andriychenko, Matthew Pocock, Darren Wilkinson, Malcolm Young and Anil Wipat

Correspondence should be addressed to:
Anil Wipat
School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne, UK. Ne1 7RU
ku.ca.lcn@nulltapiw.lina


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.

Reference

Simon J Cockell, Jochen Weile, Phillip Lord, Claire Wipat, Dmytro Andriychenko, Matthew Pocock, Darren Wilkinson, Malcolm Young and Anil Wipat. An integrated dataset for in silico drug discovery. Journal of Integrative Bioinformatics, 7(3):116, 2010. Online Journal: http://journal.imbio.de/index.php?paper_id=116
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