Cloud Computing for Chemical Activity Prediction

  1. Lookup NU author(s)
  2. Professor Paul Watson
  3. Dr Jacek Cala
  4. Vladimir Sykora
  5. Dr Hugo Hiden
  6. Dr Simon Woodman
  7. Martyn Taylor
  8. Dr Dominic Searson
Author(s)Watson P, Leahy D, Cala J, Sykora V, Hiden H, Woodman S, Taylor M, Searson D
Publication type Report
Series TitleSchool of Computing Science Technical Report Series
Legacy DateMarch 2011
Report Number1242
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This paper describes how cloud computing has been used to reduce the time taken to generate chemical activity models from years to weeks. Chemists use Quantitative Structure-Activity Relationship (QSAR) models to predict the activity of molecules. Existing Discovery Bus software builds these models automatically from datasets containing known molecular activities, using a “panel of experts” algorithm. Newly available datasets offer the prospect of generating a large number of significantly better models, but the Discovery Bus would have taken over 5 years to compute them.Fortunately, we show that the “panel of experts” algorithm is well-matched to clouds. In the paper we describe the design of a scalable, Windows Azure based infrastructure for the panel of experts pattern. We present the results of a run in which up to 100 Azure nodes were used to generate results from the new datasets in 3 weeks.
InstitutionSchool of Computing Science, University of Newcastle upon Tyne
Place PublishedNewcastle upon Tyne
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