Machine Learning for Nanozyme Development in Anti-Inflammatory Applications

The review discusses the development of nanozymes, synthetic enzymes designed to mimic natural enzymes, for anti-inflammatory applications. It emphasizes the limitations of traditional trial-and-error methods and advocates for rational design strategies, including biomimetic, computation-driven, and data-driven approaches, to improve the efficiency and specificity of nanozymes. The document highlights the role of size, morphology, oxidation state, and ligand engineering in optimizing antioxidant activities, with smaller nanozymes showing enhanced properties. Computational models and machine learning are crucial for predicting nanozyme activity, although they require experimental validation. Nanozymes have shown promise in treating inflammatory diseases like acute kidney injury, inflammatory bowel disease, acute liver injury, and neurodegenerative diseases by reducing oxidative stress and inflammation. Despite advancements, challenges remain in understanding catalytic mechanisms and standardizing performance data, necessitating further research to optimize nanozyme design and functionality for biomedical applications.