An Integrated Computational–Experimental Strategy For the Prediction of Small Molecules as GLP–1R Agonists

The glucagon-like peptide-1 receptor (GLP-1R) plays a central role in metabolic regulation and is a major therapeutic target for obesity and diabetes. Peptide agonists, like semaglutide, targeting the GLP-1R remain among the most effective regulators of glucose metabolism and appetite. Nonetheless, recent reports about weight regain have limited the effectiveness of GLP1R peptide agonists, sustaining the interest in expanding the chemical diversity of GLP-1R ligands through drug discovery strategies. However, the structural complexity and conformational plasticity of class B1 GPCRs make conventional single-method virtual screening approaches prone to bias and limited chemotype recovery. Using an integrated ligand- and structure-based virtual screening pipeline, explicitly combining complementary ligand-based descriptors, multi-fingerprint similarity, electrostatic similarity, pharmacophore modeling, and multi-conformation docking under a consensus-driven selection strategy, we were able to identify three chemically distinct classes of GLP-1R agonist candidates: GQB47810, a non-peptidic molecule; neuromedin C, a peptide, and 2,5-Pen-enkephalin (DPDPE), a small peptide. From all of them, DPDPE showed the greatest effectiveness, reaching values similar to those of GLP-1, although with lower potency. Further in vitro characterization confirmed that pen-enkephalin behaved as a full agonist and exhibited dual GLP-1R/GIPR agonistic activity. These findings establish a consensus-driven and transferable computational framework for chemotype-diverse agonist discovery at conformationally flexible GPCR targets, and revealed a pentapeptide with GLP-1-like efficacy as a promising lead for next-generation small peptide therapeutics.