Exploring structure-selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping

Abstract

We design and analyze compound selectivity sets of antagonists with differential selectivity against seven biogenic amine G-protein coupled receptors. The selectivity sets consist of a total of 267 antagonists and contain a spectrum of in part closely related molecular scaffolds. Each set represents a different selectivity profile. Using these com- pound sets, a systematic computational analysis of structure-selectivity relationships is carried out with different 2D similarity methods including fingerprints, recursive partitioning, clustering, and dynamic compound mapping. Screening calculations are performed in a background database containing nearly four million molecules. Fingerprint searching and compound mapping are found to enrich target-selective antagonists over family-selective ones. Dynamic compound mapping effectively discriminates database compounds from GPCR antagonists and consistently retains target-selective antagonists during the final dimension extension levels. Furthermore, the widely used MACCS key fingerprint displays a strong tendency to distinguish between target- and family-selective GPCR antagonists. Taken together, the results indicate that different types of 2D similarity methods are capable of distinguishing closely related molecules having different selectivity. The reported compound benchmark system is made freely available in order to enable selectivity-oriented analyses using other computational approaches.