Document Detail


Scaling predictive modeling in drug development with cloud computing.
MedLine Citation:
PMID:  25493610     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on Amazon Elastic Cloud. We train models on open datasets of varying sizes for the endpoints logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large datasets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.
Authors:
Behrooz Torabi Moghadam; Jonathan Alvarsson; Marcus Holm; Martin Eklund; Lars Carlsson; Ola Spjuth
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-12-10
Journal Detail:
Title:  Journal of chemical information and modeling     Volume:  -     ISSN:  1549-960X     ISO Abbreviation:  J Chem Inf Model     Publication Date:  2014 Dec 
Date Detail:
Created Date:  2014-12-10     Completed Date:  -     Revised Date:  2014-12-11    
Medline Journal Info:
Nlm Unique ID:  101230060     Medline TA:  J Chem Inf Model     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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