MOLECULAR AND COMPUTATIONAL APPROACHES IN DESIGNING PAN PPAR AGONISTS FOR METABOLIC DISEASE MANAGEMENT
Metabolic disorders, including type 2 diabetes mellitus, obesity, and dyslipidemia, are complex and multifactorial conditions requiring multi-target therapeutic strategies for effective management. Peroxisome proliferator-activated receptors (PPARs)—PPAR-?, PPAR-?, and PPAR-?—play a central role in regulating glucose and lipid metabolism, inflammation, and energy homeostasis, making them attractive pharmacological targets. The development of pan PPAR agonists, capable of simultaneously modulating all three isoforms, has emerged as a promising approach to achieve synergistic therapeutic effects. In recent years, molecular and computational approaches have significantly advanced the design and optimization of such multi-target agents. Structural elucidation of PPAR ligand-binding domains has enabled a deeper understanding of receptor–ligand interactions, facilitating rational drug design through structure–activity relationship (SAR) studies. Computational tools, including molecular docking, molecular dynamics simulations, and quantitative structure–activity relationship (QSAR) modeling, have accelerated lead identification and optimization by predicting binding affinity, stability, and selectivity. Additionally, artificial intelligence and machine learning techniques have enhanced the ability to analyze large chemical datasets, predict pharmacokinetic and toxicity profiles, and identify novel scaffolds with improved therapeutic potential. Despite these advancements, challenges remain in achieving balanced receptor activation while minimizing adverse effects associated with PPAR modulation. The integration of computational design with experimental validation, along with biomarker-driven and personalized therapeutic strategies, offers a comprehensive framework for the development of safer and more effective pan PPAR agonists. This review highlights the critical role of molecular and computational methodologies in shaping next-generation drug discovery and underscores their potential in addressing the growing burden of metabolic diseases