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Bulletin of the Korean Chemical Society (BKCS)

ISSN 0253-2964(Print)
ISSN 1229-5949(Online)
Volume 32, Number 4
BKCSDE 32(4)
April 20, 2011

Title	The Predictive QSAR Model for hERG Inhibitors Using Bayesian and Random Forest Classification Method
Author	Jun Hyoung Kim, Chong Hak Chae, Shin Myung Kang, Joo Yon Lee, Gil Nam Lee, Soon Hee Hwang, Nam Sook Kang *
Keywords	hERG, Classification, Bayesian, Random forest, in-silico prediction
Abstract	In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naïve Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.
Page	1237 - 1240
Full Text	PDF / Supporting Information

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