Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening...

Peroxisome proliferator-activated receptor γ (PPARγ) is a key therapeutic target for type 2 diabetes and cardiovascular diseases due to its central role in regulating glucose and lipid metabolism. While full PPARγ agonists exhibit efficacy, they are linked to adverse effects; in contrast, PPARγ partial agonists retain metabolic regulatory functions with improved safety, representing promising candidates for type 2 diabetes treatment. However, their action mechanisms and structure-activity relationships remain unclear. Herein, we developed an integrated virtual screening strategy combining fragment molecular orbital (FMO) calculations, machine learning, molecular docking, interaction fingerprint (IFP) filtering, and molecular dynamics (MD) simulations to identify potential PPARγ partial agonists and elucidate their interaction mechanisms. FMO analysis first confirmed interaction differences between PPARγ agonist classes at the binding pocket, pinpointing critical residues (CYS285, ARG288, ILE341, and SER342) for partial agonist activity. Using three machine learning algorithms (random forest, extra trees, and XGBoost) with extended connectivity fingerprints (ECFP), we constructed QSAR classification models and screened 9630 compounds. SHAP analysis highlighted key fingerprint fragments (positions 45, 1034, and 1243) governing bioactivity. Molecular docking and IFP refinement yielded six high-potency candidates, whose binding stability and partial agonist properties were validated via MD simulations, MM/PBSA binding free energy calculations, hydrogen bond analysis, and FMO calculations. Notably, these candidates did not directly interact with the AF2 domain, consistent with the canonical partial agonist mode of action. This multidisciplinary approach provides a framework for rational design of novel PPARγ partial agonists, and the identified molecules serve as promising leads for type 2 diabetes therapeutics.