Machine learning for nanozyme development in anti-inflammatory applications

Abstract

Reactive oxygen species (ROS) play pivotal roles in diverse physiological processes, exerting a significant influence on various organ systems within the human body. Recently, there has been a notable upswing in the design of nanomaterials based on natural enzymes with the ability to scavenge ROS. These nanomaterials hold promise as potent alternatives to conventional antioxidants. However, the conventional design of these materials has often relied on empirical and trial-and-error methods, posing challenges in capturing the intricate conformational relationships of nanozymes. This comprehensive review aims to consolidate rational design strategies and applications of nanozymes. Primarily, it advocates for an in-depth exploration of nanozyme mechanisms to facilitate precise design. Four rational design strategies: biomimetic design, experimental laws-driven design, computation-driven design, and data-driven design are scrutinized while considering their respective advantages, disadvantages, and application conditions. The review subsequently delves into the diverse applications of nanozymes across fields such as inflammatory diseases treatment, disease diagnosis, and environmental applications. Finally, the review outlines the challenges and prospects associated with the rational design of nanozymes while providing a comprehensive overview of this burgeoning area of research.

Keywords

anti-inflammatory application / antioxidant / machine learning / nanozyme / reactive oxygen species

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