Computational Approaches for PPARγ Inhibitor Development: Recent Advances and Perspectives

The development of peroxisome proliferator-activated receptor gamma (PPARγ) inhibitors has attracted significant interest for treating metabolic disorders, cancer, and inflammatory diseases. This review highlights the crucial role of computational modelling in advancing PPARγ inhibitor development, emphasizing how these techniques streamline the identification, optimization, and evaluation of new drug candidates. Key methods include molecular docking, QSAR, and molecular dynamics simulations, which enhance the efficiency and accuracy of inhibitor design. Computational modelling has deepened our understanding of PPARγ binding mechanisms and conformational dynamics, allowing researchers to predict and optimize ligand-receptor complex stability. Despite these advancements, challenges remain, such as improving predictions of pharmacokinetic properties (ADME) to evaluate drug-like qualities. In conclusion, computational modelling has significantly enhanced PPARγ inhibitor discovery and development, offering new opportunities to address complex diseases. Continued refinement of these models, combined with experimental validation and emerging technologies, is crucial for overcoming current limitations and achieving successful clinical outcomes.