Similarity-based SIBAR descriptors for classification of chemically diverse hERG blockers.
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.
Thai
Khac-Minh
KM
Emerging Field Pharmacoinformatics, Department of Medicinal Chemistry, University of Vienna, 1090 Vienna, Austria. thaikhacminh@gmail.com
Ecker
Gerhard F
GF
D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors* / Q000737:chemistry; D006801:Humans; D008958:Models, Molecular; D016571:Neural Networks, Computer; D026902:Potassium Channel Blockers / Q000737:chemistry* / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship
D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; C000606913:KCNH2 protein, human; D026902:Potassium Channel Blockers