The MC4R, a GPCR, has long been a major target for obesity treatment. As the most well‐studied melanocortin receptor subtype, the evolutionary knowledge pushes the drug development and structure–activity relationship (SAR) moving forward. The past decades have witnessed the evolution of scientists' view on GPCRs gradually from the control of a single canonical signalling pathway via a bilateral ‘active‐inactive’ model to a multi‐state alternative model where the ligands' binding affects the selection of the downstream signalling. This evolution brings the concept of biased signalling and the beginning of the next generation of peptide drug development, with the aim of turning from receptor subtype specificity to signalling pathway selectivity. The determination of the value structures of the MC4R revealed insights into the working mechanism of MC4R activation upon binding of agonists. However, new challenge has risen as we seek to unravel the mystery of MC4R signalling selection. Thus, more biased agonists and ligands with representative biological functions are needed to solve the rest of the puzzle.

A systematic structure–activity and computational modeling analysis of a series of glucagon-like peptide-1 receptor (GLP-1R) agonists based upon an ultra-short GLP-1 peptide, H-His-Aib-Glu-Gly-Thr-Phe-Thr-Ser-Asp-Bip-Bip-NH2, was conducted. This highly potent 11-mer peptide led to a deeper understanding of the α-helical bias of strategic α-methylation within the linear parent template as well as optimization of GLP-1R agonist potency by 1000-fold. These data were correlated with previously reported co-structures of both full-length GLP-1 analogs and progenitor N-terminal GLP-1 fragment analogs related to such ultra-short GLP-1R agonist peptides. Furthermore, the development of a quantitative structure–activity relationship (QSAR) model to analyze these findings is described in this study.