School of Medicine, and State, Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300071, China.
Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China.
Adv Mater. 2024 Mar;36(10):e2210848. doi: 10.1002/adma.202210848. Epub 2023 May 10.
Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.
纳米酶是具有类酶仿生特性的纳米材料。结合纳米材料的固有特性，纳米酶在材料科学、化学工程、生物工程、生物化学和疾病治疗诊断学等领域具有广泛的应用。最近，发表结果的异质性突出了纳米酶在催化能力一致性方面的复杂性和多样性。机器学习 (ML) 在发现新材料方面显示出了巨大的潜力，但基于 ML 方法设计新型纳米酶仍然具有挑战性。相反，ML 用于促进催化材料和工程酶的智能设计和应用的优化。成功的 ML 算法在催化材料和工程酶的智能设计中的应用，可以同时促进具有理想特性的下一代纳米酶的有针对性的开发。在这里，总结了 ML 在催化材料和酶设计中的应用以及新兴 ML 应用如何作为克服纳米酶研发中耗时费力的测试相关挑战的有前途的策略方面的最新进展。还强调了成功的 ML 辅助催化材料和工程酶的应用在纳米酶设计中的潜在应用，特别关注增强底物选择性和催化活性的设计和再现的统一目标。
