Prediction of hERG K+ channel blockage using deep neural networks

Chemical Biology & Drug Design
Yanmin ZhangHaichun Liu

Abstract

Human ether-a-go-go-related gene (hERG) K+ channel blockage may cause severe cardiac side-effects and has become a serious issue in safety evaluation of drug candidates. Therefore, improving the ability to avoid undesirable hERG activity in the early stage of drug discovery is of significant importance. The purpose of this study was to build predictive models of hERG activity by deep neural networks. For each combination of sampling methods and descriptors, deep neural networks with different architectures were implemented to build classification models. The optimal model M15 with three hidden layers, undersampling method, and 2D descriptors yielded the prediction accuracy of 0.78 and F1 score of 0.75 on the test set as well as accuracy of 0.77 and F1 score of 0.34 on the external validation set, outperforming the other 35 models including 9 random forest models. Particularly, the optimal model M15 achieved the highest F1 score and the second highest accuracy when compared with other five methods from four groups using different machine learning algorithms with the same external validation set. It can be believed that this model has powerful capability on prediction of hERG toxicity, which is of great benefit for developing nov...Continue Reading

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Citations

Jun 10, 2020·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Kwang-Eun ChoiNam Sook Kang
Dec 29, 2020·Chemical Research in Toxicology·Marcus W H WangTimothy E H Allen
Mar 2, 2021·Experimental Biology and Medicine·Siyu ZhuJing An
Jul 3, 2021·ELife·Parya AghasafariColleen E Clancy
Sep 11, 2021·Journal of Chemical Information and Modeling·Teresa Maria CreanzaGiuseppe Felice Mangiatordi

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