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Support Vector Machines in HTS Data Mining: Type I MetAPs Inhibition Study

Bioinformatics Core Facility, Information and Telecommunication Technology Center, University of Kansas, Lawrence

Yinghua Dong

Bioinformatics Core Facility, University of Kansas, Lawrence

Gerald H. Lushington

Bioinformatics Core Facility, Molecular Graphics and Modeling Laboratory, University of Kansas, Lawrence

Qi-Zhuang Ye

High Throughput Screening Laboratory, University of Kansas, Lawrence

Gunda I. Georg

High Throughput Screening Laboratory, University of Kansas, Lawrence

This article reports a successful application of support vector machines (SVMs) in mining high-throughput screening (HTS) data of a type I methionine aminopeptidases (MetAPs) inhibition study. A library with 43,736 small organic molecules was used in the study, and 1355 compounds in the library with 40% or higher inhibition activity were considered as active. The data set was randomly split into a training set and a test set (3:1 ratio). The authors were able to rank compounds in the test set using their decision values predicted by SVM models that were built on the training set. They defined a novel score PT₅₀, the percentage of the test set needed to be screened to recover 50% of the actives, to measure the performance of the models. With carefully selected parameters, SVM models increased the hit rates significantly, and 50% of the active compounds could be recovered by screening just 7% of the test set. The authors found that the size of the training set played a significant role in the performance of the models. A training set with 10,000 member compounds is likely the minimum size required to build a model with reasonable predictive power.

Key Words: support vector machines • high-throughput screening • MetAP • machine learning

This version was published on March 1, 2006

Journal of Biomolecular Screening, Vol. 11, No. 2, 138-144 (2006)
DOI: 10.1177/1087057105284334


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