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Publication:

Cloud Computing for Chemical Activity Prediction (2011)

Author(s): Watson P, Leahy D, Cala J, Sykora V, Hiden H, Woodman S, Taylor M, Searson D

    Abstract: 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.

      • Date: March 2011
      • Series Title: School of Computing Science Technical Report Series
      • Pages: 11
      • Institution: School of Computing Science, University of Newcastle upon Tyne
      • Publication type: Report
      • Bibliographic status: Published

      Keywords: cloud computing, QSAR, Windows Azure

      Staff

      Professor Paul Watson
      Professor of Computing Science