Original Article

Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

Authors: Li-li LIU, Jing LU, Yin LU, Ming-yue ZHENG, Xiao-min LUO, Wei-liang ZHU, Hua-liang JIANG, Kai-xian CHEN
DOI: 10.1038/aps.2014.35


Li-li LIU1, Jing LU1, 2, Yin LU1, Ming-yue ZHENG1, *, Xiao-min LUO1, *, Wei-liang ZHU1, Hua-liang JIANG1, 3, Kai-xian CHEN1
1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; 2Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Yantai 264005, China; 3School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China

Aim: A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels.
Methods: Doddareddy’s hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy’s experimentally validated dataset with 60 compounds was used for external test set validation.

Results: A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation.

Conclusion: The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.

Keywords: hERG; potassium channels; long QT syndrome; pharmacophore; modeling; Laplacian-modified Bayesian; extended-connectivity fingerprints; QSAR

This work was supported by Hi-TECH Research and Develop­ment Program of China (Grant 2012AA020308), National S&T Major Project (Grant 2012ZX09301, 2014ZX09507002), and National Natural Science Foundation of China (81220108025, 81001399, 2013ZX09507001).
* To whom correspondence should be addressed.
E-mail myzheng@mail.shcnc.ac.cn (Ming-yue ZHENG); xmluo@mail.shcnc.ac.cn (Xiao-min LUO)
Received 2014-01-24 Accepted 2014-04-10

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