Computational Discovery of Novel SGLT2 Inhibitors from Eight Selected Medicine Food Homology Herbs Using a Multi-Stage Virtual Screening Pipeline

Background/Objectives: Sodium-glucose co-transporter 2 (SGLT2) inhibitors are essential antidiabetic medications. However, their side effects warrant careful consideration. The search for novel SGLT2 inhibitors with high affinity remains an ongoing endeavor. Medicine food homology (MFH) herbs show promise for drug development due to their nutritional and medicinal value. Methods: This study aims to address the shortcomings of existing virtual screening models for SGLT2 inhibitors by optimizing feature selection and integrating multidimensional molecular fingerprints. Subsequently, an integrated virtual screening pipeline is constructed to identify potential SGLT2 inhibitors from eight selected MFH herbs. Results: The results indicate that the optimal model (LightGBM and RF) achieved an accuracy of 0.97 and an AUC of 0.98. Following rigorous filtering, a total of 44 potential SGLT2 inhibitors were identified, among which, Isoononin (from Gancao) and Ononin (from Huangqi, Gegen, and Gancao) exhibit favorable drug likeness and safety. Molecular docking demonstrate that both compounds can effectively bind to the SGLT2 active site, establishing stable hydrophobic interactions with critical residues such as Phe98 and Phe453. Furthermore, molecular dynamics simulations confirm the stability of the interactions between the two compounds and SGLT2. Conclusions: This study significantly enhances the accuracy and stability of SGLT2 inhibitor virtual screening models by addressing deficiencies in structural characterization and feature selection. It provides candidate molecules for the development of novel SGLT2 inhibitors and offers new scientific evidence for the application of MFH herbs in the prevention and treatment of chronic metabolic diseases.