Similarity-based classifier using topomers to provide a knowledge base for hERG channel inhibition.
In a proof of principle study we show that the similarity property principle can be applied to predict ADMET properties, exemplified on the case of hERG K+ channel inhibition. Early prediction of a drug candidate's hERG channel activity is becoming increasingly important in the drug discovery process because blockade of the hERG channel may lead to life-threatening cardiac arrhythmias. Using the Tripos Topomer Search technology as compound similarity measure, we query molecules with unknown hERG activity for similar molecules in a set of compounds with known hERG activity. The hERG activity of the query molecule is then predicted based on the hERG activity of its Topomer Search neighbors and their distances to the query molecule. The similarity property principle can be applied with promising performance to predict hERG inhibition as long as there is a high structural overlap between the chemical spaces of the query compounds and the reference data set. We show that this is achievable for database sizes of about 10,000 structurally diverse molecules. In this case topoHERG is a similarity-based hERG classifier, which also acts as a knowledge base for hERG channel inhibition.
Bayer HealthCare AG, Global Drug Discovery, Lead Generation and Optimization, Aprather Weg 18a, D-42096 Wuppertal, Germany.
Britta
B
Nisius
Andreas H
AH
Göller
D000818:Animals; D002460:Cell Line; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors*; D006801:Humans; D051188:Knowledge Bases
D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; C000606913:KCNH2 protein, human