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Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

文献类型：期刊论文

作者Liu, Li-li2; Lu, Jing2,3; Lu, Yin2; Zheng, Ming-yue2; Luo, Xiao-min2; Zhu, Wei-liang2; Jiang, Hua-liang1,2; Chen, Kai-xian2
刊名ACTA PHARMACOLOGICA SINICA
出版日期2014-08
卷号35期号:8页码:1093-1102
关键词hERG potassium channels long QT syndrome pharmacophore modeling Laplacian-modified Bayesian extended-connectivity fingerprints QSAR
ISSN号1671-4083
DOI10.1038/aps.2014.35
文献子类Article
英文摘要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 (M-w, PPSA, ALogP and pK(a)_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.
WOS关键词LONG-QT-SYNDROME ; VECTOR MACHINE METHOD ; K+ CHANNEL ; IN-SILICO ; QSAR MODEL ; BLOCKERS ; INHIBITION ; BINDING ; DESCRIPTORS ; INSIGHTS
资助项目Hi-TECH Research and Development Program of China[2012AA020308] ; National ST Major Project[2012ZX09301] ; National ST Major Project[2014ZX09507002] ; National Natural Science Foundation of China[81220108025] ; National Natural Science Foundation of China[81001399] ; National Natural Science Foundation of China[2013ZX09507001]
WOS研究方向Chemistry ; Pharmacology & Pharmacy
语种英语
CSCD记录号CSCD:5206244
WOS记录号WOS:000340510400012
出版者ACTA PHARMACOLOGICA SINICA
源URL[http://119.78.100.183/handle/2S10ELR8/276961]  
专题药物发现与设计中心
中科院受体结构与功能重点实验室
信息中心
新药研究国家重点实验室
通讯作者Zheng, Ming-yue
作者单位1.E China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & Design Ctr, Shanghai 201203, Peoples R China;
3.Yantai Univ, Sch Pharm, Dept Med Chem, Yantai 264005, Peoples R China;
推荐引用方式
GB/T 7714		 Liu, Li-li,Lu, Jing,Lu, Yin,et al. Novel Bayesian classification models for predicting compounds blocking hERG potassium channels[J]. ACTA PHARMACOLOGICA SINICA,2014,35(8):1093-1102.
APA Liu, Li-li.,Lu, Jing.,Lu, Yin.,Zheng, Ming-yue.,Luo, Xiao-min.,...&Chen, Kai-xian.(2014).Novel Bayesian classification models for predicting compounds blocking hERG potassium channels.ACTA PHARMACOLOGICA SINICA,35(8),1093-1102.
MLA Liu, Li-li,et al."Novel Bayesian classification models for predicting compounds blocking hERG potassium channels".ACTA PHARMACOLOGICA SINICA 35.8(2014):1093-1102.

入库方式： OAI收割

来源：上海药物研究所

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