PyRod Enables Rational Homology Model-based Virtual Screening Against MCHR1

Discovery of Novel, Selective, and Nonbasic Agonists for the Kappa-Opioid Receptor Determined by Salvinorin A-Based Virtual Screening

Modulating the kappa-opioid receptor (KOR) is a promising strategy for treating various human diseases. KOR agonists show potential for treating pain, pruritus, and epilepsy, while KOR antagonists show potential for treating depression, anxiety, and addiction. The diterpenoid Salvinorin A (SalA), a secondary metabolite of Salvia divinorum, is a potent and selective KOR agonist. Unlike typical opioids, SalA lacks a basic nitrogen, which encouraged us to search for nonbasic KOR ligands. Through structure-based virtual screening using 3D pharmacophore models based on the binding mode of SalA, we identified novel, nonbasic, potent, and selective KOR agonists. In vitro studies confirmed two virtual hits, SalA-VS-07 and SalA-VS-08, as highly selective for the KOR and showing G protein-biased KOR agonist activity. Both KOR ligands share a novel spiro-moiety and a nonbasic scaffold. Our findings provide novel starting points for developing therapeutics aimed at treating pain and other conditions in which KOR is a central player.

Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.