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Similarity-based SIBAR descriptors for classification of chemically diverse hERG blockers

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Abstract

There is an increasing interest in computational models for the classification and prediction of the human ether-a-go-go-related-gene (hERG) potassium channel affinity in the early phase of drug discovery and development. In this study, similarity-based SIBAR descriptors were applied in order to develop and validate in silico binary QSAR and counter-propagation neural network models for the classification of hERG activity. The SIBAR descriptors were calculated based on four reference datasets using four sets of 2D- and 3D-descriptors including 3D-grid-based VolSurf, 3D ‘inductive’ QSAR, Van der Waals surface area (P_VSA) and a set of 11 hERG relevant 2D descriptors devised from feature selection methods. The results indicate that the reference data set tailored to the specific problem, together with a set of hERG relevant descriptors, provides highly predictive models.

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  1. Khac-Minh Thai

    Present address: Department of Pharmaceutical Chemistry, School of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang St., Dist. 1, Ho Chi Minh City, Vietnam

Authors and Affiliations

  1. Emerging Field Pharmacoinformatics, Department of Medicinal Chemistry, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria

    Khac-Minh Thai & Gerhard F. Ecker

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  1. Khac-Minh Thai
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Correspondence to Gerhard F. Ecker.

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Thai, KM., Ecker, G.F. Similarity-based SIBAR descriptors for classification of chemically diverse hERG blockers. Mol Divers 13, 321–336 (2009). https://doi.org/10.1007/s11030-009-9117-0

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