Cloud Computing for Chemical Activity Prediction

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. © 2011 Newcastle University. Printed and published by the Newcastle University, Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, England. Bibliographical details WATSON, P., LEAHY, D., CALA, J., SYKORA, V., HIDEN, H., WOODMAN, S., TAYLOR, M., SEARSON, D. Cloud Computing for Chemical Activity Prediction [By] P. Watson, D. Leahy, J. Cala, V. Sykora, H. Hiden, S. Woodman, M. Taylor, D. Searson Newcastle upon Tyne: Newcastle University: Computing Science, 2011. (Newcastle University, Computing Science, Technical Report Series, No. CS-TR-1242)