Towards in silico identification of the human ether-a-go-go-related gene channel blockers: discriminative vs. generative classification models

Abstract

HERG potassium channels have a critical role in the normal electrical activity of the heart. The blockade of hERG channels in heart cells can result in a potentially fatal disorder called long QT syndrome. HERG channels can be blocked by compounds with diverse structures belonging to several drug classes. Presented herein are generative (Generative Topographic Maps) and discriminative (Support Vector Machines) classification models to categorize the compounds in silico into active and inactive classes by using different types of descriptors. The predictive performance of discriminative and generative classification models has been compared. Here, the possibility of using Generative Topographic Maps as an approach for applicability domain analysis and to generate probability-based descriptors was demonstrated to our knowledge for the first time. Comparison of obtained results with the models developed by other teams on the same data set has been performed.

Keywords:

• human ether-a-go-go-related gene
• quantitative structure-activity relationships
• classification models for hERG inhibitors
• generative topographic maps
• support vector machines
• generative models
• discriminative models

Acknowledgments

The authors thank the Russian Foundation for Basic Research (projects no. 11-03-00161 and 12-03-33086), GDRE SupraChem, ARCUS project, CNRS and the French Embassy in Russia for the support. The authors acknowledge Professor Alexandre Varnek (Université de Strasbourg) and Professor Igor Baskin (Moscow State University) for fruitful discussions.