An efficient computational method for hit and lead identification is described. The method that incorporate ligand information from physicogenetically related 7TM receptors, i.e. receptors with similar physicochemical features in the ligand binding pockets, have been developed to aid the construction of pharmacophore queries for mining of vendor and in-house databases to produce small focused libraries for a specific GPCR target. The physicogenetically related targets could be complementary to phylogenetically derived receptors and convey more relevance for the structure-based design approaches suitable for GPCR targets associated with no or limited ligand information. The approach is useful not only in identification of hits but also in the hit-to-lead process as constructed homology receptor models, SAR information and pharmacophore features are collectively utilized in the design of proprietary new lead series. This site-directed drug discovery approach of making smaller receptor-specific libraries displays important advantages over conventional HTSbased generation of hits. The methodology has been exemplified with the CRTH2 receptor, which was associated with minimal ligand information, to produce a small diverse library containing several useful hit series which were further converted into drugable lead series. The use of ligand and QSAR information in scaffold hopping was exemplified with MCH1R antagonists, which had been obtained via chemogenomics-enriched design. Finally, an example on how ligand relationships can be used in identifying receptor relationships was given with CCR2 antagonists to highlight the 3D relationships of GPCR targets not directly evident from either phylogenetic or physicogenetic relationships.
Keywords: 7TM receptor, beta2 receptor, CRTH2 receptor, MCH1 receptor, binding pocket, physicogenetics, chemogenomics, virtual screening, scaffold, MCH1R antagonists, GPCR targets, G Protein-Coupled Receptors, X-ray crystallography, highhroughput screening (HTS), fragment-based drug design (FBDD)
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