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• Bulletin of the Korean Chemical Society
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• Bulletin of the Korean Chemical Society
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• 2006

• Proceedings of the Korean Society for Applied Microbiology Conference
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• Bulletin of the Korean Chemical Society
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• Bulletin of the Korean Chemical Society
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• 2011

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.

• Bulletin of the Korean Chemical Society
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• v.27 no.9
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• pp.1285-1286
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• 2006

• Proceedings of the Materials Research Society of Korea Conference
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• 2006.05a
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• pp.27.1-27.1
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• 2006

• Proceedings of the Membrane Society of Korea Conference
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• 2004.11a
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• 2004