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  • Title: A model for identifying HERG K+ channel blockers.
    Author: Aronov AM, Goldman BB.
    Journal: Bioorg Med Chem; 2004 May 01; 12(9):2307-15. PubMed ID: 15080928.
    Abstract:
    Acquired long QT syndrome (LQTS) occurs frequently as a side effect of blockade of cardiac HERG K(+) channels by commonly used medications. A large number of structurally diverse compounds have been shown to inhibit K(+) current through HERG. There is considerable interest in developing in silico tools to filter out potential HERG blockers early in the drug discovery process. We describe a binary classification model that combines a 2D topological similarity filter with a 3D pharmacophore ensemble procedure to discriminate between HERG actives and inactives with an overall accuracy of 82%, with false negative and false positive rates of 29% and 15%, respectively. This model should be generally applicable in virtual library counterscreening against HERG.
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