Using iterative MapReduce for parallel virtual screening
2013 (English)In: 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom), IEEE Computer Society, 2013, 27-32 p.Conference paper (Refereed)
Virtual Screening is a technique in chemo informatics used for Drug discovery by searching large libraries of molecule structures. Virtual Screening often uses SVM, a supervised machine learning technique used for regression and classification analysis. Virtual screening using SVM not only involves huge datasets, but it is also compute expensive with a complexity that can grow at least up to O(n2). SVM based applications most commonly use MPI, which becomes complex and impractical with large datasets. As an alternative to MPI, MapReduce, and its different implementations, have been successfully used on commodity clusters for analysis of data for problems with very large datasets. Due to the large libraries of molecule structures in virtual screening, it becomes a good candidate for MapReduce. In this paper we present a MapReduce implementation of SVM based virtual screening, using Spark, an iterative MapReduce programming model. We show that our implementation has a good scaling behaviour and opens up the possibility of using huge public cloud infrastructures efficiently for virtual screening.
Place, publisher, year, edition, pages
IEEE Computer Society, 2013. 27-32 p.
Big Data, Chemoinformatics, MapReduce, Parallel SVM, Spark
IdentifiersURN: urn:nbn:se:kth:diva-146956DOI: 10.1109/CloudCom.2013.99ISI: 000352079100005ScopusID: 2-s2.0-84899736110ISBN: 978-0-7695-5095-4OAI: oai:DiVA.org:kth-146956DiVA: diva2:726873
5th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2013; Bristol; United Kingdom; 2 December 2013 through 5 December 2013
QC 201406192014-06-192014-06-182014-06-19Bibliographically approved