Activity Prediction of Hormone-Sensitive Lipase Inhibitors Based on Machine Learning Methods

Hormone-sensitive lipase (HSL) is known as the key rate-limiting enzyme responsible for regulating free fatty acids (FFAs) metabolism in adipose tissue. Recently, HSL has been found to be useful in the treatment of diabetes so the discovery of new HSL inhibitors (HSLIs) is of interest. Methods for the prediction of HSLIs are highly desired to facilitate the design of novel diabetes therapeutic agents because limited knowledge exists concerning the mechanism and three dimensional (3D) structure of hormone-sensitive lipase. We have explored several machine teaming methods (support vector machines (SVM), k-nearest neighbor (k-NN), and C4.5 decision tree (C4.5 DT)) to predict desirable HSLIs from a comprehensive set of known HSLIs and non-HSLIs. Our prediction system was tested using 252 compounds (123 HSLIs and 1.29 non-HSLIs) and these are significantly more diverse in chemical structure than those in other studies. The recursive feature elimination selection method was used to improve the prediction accuracy and to select the molecular descriptors responsible for distinguishing HSLIs and non-HSLIs. Prediction accuracies were 85.7%-90.5% for HSLIs, 63.2%-68.4% for non-HSLIs, and 75.0%-80.0% for all structures based on three kinds of machine teaming methods using an independent validation set. SVM gave the best total accuracy of 80.0% for all the structures. This work suggests that machine teaming methods such as SVM are useful to predict the potential HSLIs among unknown sets of compounds and to characterize the molecular descriptors associated with HSLIs.