Scaling Predictive Modeling in Drug Development with Cloud Computing

被引:10
|
作者
Moghadam, Behrooz Torabi [1 ]
Alvarsson, Jonathan [1 ]
Holm, Marcus [2 ]
Eklund, Martin [1 ]
Carlsson, Lars [4 ]
Spjuth, Ola [1 ,3 ]
机构
[1] Uppsala Univ, Dept Pharmaceut Biosci, SE-75124 Uppsala, Sweden
[2] Uppsala Univ, Dept Informat Technol, SE-75124 Uppsala, Sweden
[3] Uppsala Univ, Sci Life Lab, SE-75124 Uppsala, Sweden
[4] AstraZeneca Innovat Med & Early Dev, DSM, Computat ADME & Safety, SE-43183 Molndal, Sweden
基金
瑞典研究理事会;
关键词
SIGNATURE MOLECULAR DESCRIPTOR; DEVELOPMENT KIT CDK; SOURCE [!text type='JAVA']JAVA[!/text] LIBRARY; QUANTITATIVE STRUCTURE; AQUEOUS SOLUBILITY; HIGH-PERFORMANCE; QSAR; DISCOVERY; CARCINOGENICITY; FRAMEWORK;
D O I
10.1021/ci500580y
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
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.
引用
收藏
页码:19 / 25
页数:7
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