Cloud Computing for Chemical Activity Prediction
Paul Watson, David E. Leahy, Jacek Cała, Sýkora, Hugo Hiden, Simon Woodman, Mark Taylor, Dominic P. Searson · 2011
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.