title: Novel Bayesian classification models for predicting compounds blocking hERG potassium channels.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.
authors: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
authors: Li-li
authors: LL
authors: Liu
authors: 1] Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China [2] Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Yantai 264005, China.
authors: Jing
authors: J
authors: Lu
authors: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
authors: Yin
authors: Y
authors: Lu
authors: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
authors: Ming-yue
authors: MY
authors: Zheng
authors: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
authors: Xiao-min
authors: XM
authors: Luo
authors: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
authors: Wei-liang
authors: WL
authors: Zhu
authors: 1] Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China [2] School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors: Hua-liang
authors: HL
authors: Jiang
authors: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
authors: Kai-xian
authors: KX
authors: Chen
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D062313:Databases, Pharmaceutical; D055808:Drug Discovery / Q000379:methods*; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors* / Q000378:metabolism; D006801:Humans; D008954:Models, Biological
chemical_list: D051638:Ether-A-Go-Go Potassium Channels