Support vector machines in HTS data mining: Type I MetAPs inhibition study.
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.
Fang
Jianwen
J
Bioinformatics Core Facility, University of Kansas, Lawrence, KS 66045, USA. jwfang@ku.edu
Dong
Yinghua
Y
Lushington
Gerald H
GH
Ye
Qi-Zhuang
QZ
Georg
Gunda I
GI
D000465:Algorithms; D000626:Aminopeptidases / Q000037:antagonists & inhibitors / Q000378:metabolism*; D017076:Computer-Aided Design; D045726:Metalloproteases / Q000502:physiology*; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
D045726:Metalloproteases; D000626:Aminopeptidases