Interpretable QSAR modelling for PPAR-γ agonist prediction by integrating a stacking strategy, docking, and MD simulations.

Peroxisome proliferator-activated receptor gamma (PPAR-γ) is a ligand-activated nuclear receptor involved in adipogenesis, glucose homeostasis, lipid metabolism, and inflammation, making it an important therapeutic target for metabolic disorders. However, the complex pharmacology of PPAR-γ presents significant challenges for rational drug discovery. In this study, we developed Meta-iPPAR, an integrative in silico framework combining stacked machine learning, molecular docking, and molecular dynamics (MD) simulations for the identification of PPAR-γ agonists. Meta-iPPAR was constructed using diverse SMILES-based molecular descriptors and multiple machine learning algorithms integrated through a stacking strategy. The proposed model achieved strong predictive performance on the independent test set, with an ACC of 0.926, AUC of 0.965, and MCC of 0.848. Scaffold analysis and SHAP interpretation further identified important chemotypes and molecular features associated with PPAR-γ activation. Large-scale virtual screening of more than 36,000 compounds from the natural product atlas identified three promising fungal-derived candidates. Subsequent docking and 300 ns MD simulations demonstrated stable binding conformations and favorable interactions with key residues in the PPAR-γ ligand-binding domain, comparable to known agonists and co-crystal ligands. Collectively, these findings suggest that Meta-iPPAR provides a reliable computational framework for screening and prioritizing potential PPAR-γ agonists in early-stage drug discovery. Future experimental validation and biological evaluation of the identified compounds are warranted. We anticipate that Meta-iPPAR will be an effective computational tool for screening and prioritizing potential compounds targeting PPAR-γ in the early stage of drug development pipelines.