Abstract：
: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 the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points 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 data sets 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.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Moghadam BT,Alvarsson J,Holm M,Eklund M,Carlsson L,Spjuth Odoi
10.1021/ci500580ysubject
Has Abstractpub_date
2015-01-26 00:00:00pages
19-25issue
1eissn
1549-9596issn
1549-960Xjournal_volume
55pub_type
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