Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain. [Display omitted] The predictive model for SGLT2 inhibitors was constructed by machine learning.The SHAP algorithm identifies important molecular fragments.The computed binding energy has high correlation with experimental values.The binding free energy decomposition determines key interaction residues.