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http://dx.doi.org/10.5012/bkcs.2011.32.4.1237

The Predictive QSAR Model for hERG Inhibitors Using Bayesian and Random Forest Classification Method  

Kim, Jun-Hyoung (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology)
Chae, Chong-Hak (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology)
Kang, Shin-Myung (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology)
Lee, Joo-Yon (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology)
Lee, Gil-Nam (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology)
Hwang, Soon-Hee (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology)
Kang, Nam-Sook (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology)
Publication Information
Bulletin of the Korean Chemical Society / v.32, no.4, 2011 , pp. 1237-1240 More about this Journal
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 naive Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.
Keywords
hERG; Classification; Bayesian; Random forest; in-silico prediction;
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