Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer’s Disease

Abstract

The dual activating potential of peroxisome proliferator-activated receptors (PPAR) α and γ offers a promising way to address metabolic issues, neuroinflammation, and protein balance in the development of Alzheimer’s disease (AD). The current study intends to underscore the emerging importance of artificial intelligence (AI) in the accelerated discovery and optimization of dual PPAR α/γ modulators based on machine learning (ML), deep learning (DL), and generative modeling. The role of autonomous and agentic-AI systems in hypothesis generation, lead optimization, and closed-loop screening processes is highlighted. In addition, AI-enabled digital twin frameworks are emerging as powerful tools to integrate multiomics, neuroimaging, and clinical data for virtual modeling of disease progression and therapeutic response. Furthermore, the importance of explainable AI (XAI) in improving the interpretability and mechanistic insights of drug-receptor interaction models is underscored. Taken together, this review integrates recent studies illustrating the utility of AI-optimized dual PPAR α/γ agonists in enhancing lead selection, dose–response prediction, and therapeutic outcome prediction, thus bridging pharmacological computation and neuroimmune targeting for next-generation precision medicine approaches in AD therapy.