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Cloud Computing for Chemical Activity Prediction

Lookup NU author(s): Professor Paul WatsonORCiD, Dr Jacek CalaORCiD, Vladimir Sykora, Dr Hugo Hiden, Dr Simon Woodman, Martyn Taylor, Dr Dominic Searson



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.

Publication metadata

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

Publication type: Report

Publication status: Published

Series Title: School of Computing Science Technical Report Series

Year: 2011

Pages: 11

Print publication date: 01/03/2011

Source Publication Date: March 2011

Report Number: 1242

Institution: School of Computing Science, University of Newcastle upon Tyne

Place Published: Newcastle upon Tyne