Nanozymes, a distinctive class of nanomaterials endowed with enzyme-like activity and kinetics akin to enzyme-catalysed reactions, present several advantages over natural enzymes, including cost-effectiveness, heightened stability, and adjustable activity. However, the conventional trial-and-error methodology for developing novel nanozymes encounters growing challenges as research progresses. The advent of artificial intelligence (AI), particularly machine learning (ML), has ushered in innovative design approaches for researchers in this domain. This review delves into the burgeoning role of ML in nanozyme research, elucidating the advancements achieved through ML applications. The review explores successful instances of ML in nanozyme design and implementation, providing a comprehensive overview of the evolving landscape. A roadmap for ML-assisted nanozyme research is outlined, offering a universal guideline for research in this field. In the end, the review concludes with an analysis of challenges encountered and anticipates future directions for ML in nanozyme research. The synthesis of knowledge in this review aims to foster a cross-disciplinary study, propelling the revolutionary field forward.

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纳米酶中的机器学习：从设计到应用

纳米酶是一类独特的纳米材料，具有类似酶的活性和类似于酶催化反应的动力学，与天然酶相比具有多种优势，包括成本效益、更高的稳定性和可调节的活性。然而，随着研究的进展，开发新型纳米酶的传统试错方法遇到了越来越大的挑战。人工智能 (AI)，特别是机器学习 (ML) 的出现，为该领域的研究人员带来了创新的设计方法。这篇综述深入探讨了机器学习在纳米酶研究中的新兴作用，阐明了通过机器学习应用所取得的进步。该综述探讨了纳米酶设计和实施中机器学习的成功实例，全面概述了不断发展的前景。概述了机器学习辅助纳米酶研究的路线图，为该领域的研究提供了通用指南。最后，该评论分析了所遇到的挑战，并预测了纳米酶研究中机器学习的未来方向。本次综述中的知识综合旨在促进跨学科研究，推动革命领域向前发展。