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Cloud Computing for Chemical Activity Prediction

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

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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.


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

URL: http://www.cs.ncl.ac.uk/publications/trs/papers/1242.pdf


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