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• W2081803746 abstract "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.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.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.The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction." @default.
• W2081803746 created "2016-06-24" @default.
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• W2081803746 date "2014-06-30" @default.
• W2081803746 modified "2023-10-16" @default.
• W2081803746 title "Novel Bayesian classification models for predicting compounds blocking hERG potassium channels" @default.
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• W2081803746 doi "https://doi.org/10.1038/aps.2014.35" @default.
• W2081803746 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4125710" @default.
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