title: A model for identifying HERG K+ channel blockers.
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.
authors.affiliation: Vertex Pharmaceuticals Inc., Cambridge, MA 02139-4242, USA. alex_aronov@vrtx.com
authors.forename: Alex M
authors.initials: AM
authors.lastname: Aronov
authors.forename: Brian B
authors.initials: BB
authors.lastname: Goldman
mesh_terms: D005188:False Negative Reactions; D005189:False Positive Reactions; D006801:Humans; D008133:Long QT Syndrome / Q000209:etiology*; D008956:Models, Chemical