As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure–function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

Azobenzene-embedded photoswitchable ligands are the widely used chemical tools in photopharmacological studies. Current approaches to azobenzene introduction rely mainly on the isosteric replacement of typical azologable groups. However, atypical scaffolds may offer more opportunities for photoswitch remodeling, which are chemically in an overwhelming majority. Herein, we investigate the rational remodeling of atypical scaffolds for azobenzene introduction, as exemplified in the development of photoswitchable ligands for the cannabinoid receptor 2 (CB2). Based on the analysis of residue-type clusters surrounding the binding pocket, we conclude that among the three representative atypical arms of the CB2 antagonist, AM10257, the adamantyl arm is the most appropriate for azobenzene remodeling. The optimizing spacer length and attachment position revealed AzoLig 9 with excellent thermal bistability, decent photopharmacological switchability between its two configurations, and high subtype selectivity. This structure-guided approach gave new impetus in the extension of new chemical spaces for tool customization for increasingly diversified photo-pharmacological studies and beyond.

Glucagon-like peptide-1 receptor (GLP-1R) is a critical therapeutic target for type 2 diabetes mellitus (T2DM). The GLP-1R cellular signaling mechanism relevant to insulin secretion and blood glucose regulation has been extensively studied. Numerous drugs targeting GLP-1R have entered clinical treatment. However, novel functional molecules with reduced side effects and enhanced therapeutic efficacy are still in high demand. In this review, we summarize the basis of GLP-1R cellular signaling, and how it is involved in the treatment of T2DM. We review the functional molecules of incretin therapy in various stages of clinical trials. We also outline the current strategies and emerging techniques that are furthering the development of novel therapeutic drugs for T2DM and other metabolic diseases.

Purpose To provide a complex‐valued deep learning approach for partial Fourier (PF) reconstruction of complex MR images. Methods Conventional PF reconstruction methods, such as projection onto convex sets (POCS), uses low‐resolution image phase information from the central symmetrically sampled k‐space for image reconstruction. However, this smooth phase constraint undermines the phase estimation accuracy in presence of rapid local phase variations, causing image artifacts and limiting the extent of PF reconstruction. Using both magnitude and phase characteristics in big complex image datasets, we propose a complex‐valued deep learning approach with an unrolled network architecture for PF reconstruction that iteratively reconstructs PF sampled data and enforces data consistency. We evaluate our approach for reconstructing both spin‐echo and gradient‐echo data. Results The proposed method outperformed the iterative POCS PF reconstruction method. It produced better artifact suppression and recovery of both image magnitude and phase details in presence of local phase changes. No noise amplification was observed even for highly PF reconstruction. Moreover, the network trained on axial brain data could reconstruct sagittal and coronal brain and knee data. This method could be extended to 2D PF reconstruction and joint multi‐slice PF reconstruction. Conclusion Our proposed method can effectively reconstruct MR data even at low PF fractions, yielding high‐fidelity magnitude and phase images. It presents a valuable alternative to conventional PF reconstruction, especially for phase‐sensitive 2D or 3D MRI applications.