Park Yeong-Seo, Park Byeong Uk, Jeon Hee-Jae
Department of Advanced Mechanical Engineering, Kangwon National University, Chuncheon, Republic of Korea.
Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, Republic of Korea.
Front Chem. 2024 Oct 17;12:1483986. doi: 10.3389/fchem.2024.1483986. eCollection 2024.
Nanozymes, synthetic nanomaterials that mimic the catalytic functions of natural enzymes, have emerged as transformative technologies for biosensing, diagnostics, and environmental monitoring. Since their introduction, nanozymes have rapidly evolved with significant advancements in their design and applications, particularly through the integration of machine learning (ML). Machine learning (ML) has optimized nanozyme efficiency by predicting ideal size, shape, and surface chemistry, reducing experimental time and resources. This review explores the rapid advancements in nanozyme technology, highlighting the role of ML in improving performance across various bioapplications, including real-time monitoring and the development of chemiluminescent, electrochemical and colorimetric sensors. We discuss the evolution of different types of nanozymes, their catalytic mechanisms, and the impact of ML on their property optimization. Furthermore, this review addresses challenges related to data quality, scalability, and standardization, while highlighting future directions for ML-driven nanozyme development. By examining recent innovations, this review highlights the potential of combining nanozymes with ML to drive the development of next-generation diagnostic and detection technologies.
纳米酶是一类模拟天然酶催化功能的合成纳米材料，已成为生物传感、诊断和环境监测等领域的变革性技术。自问世以来，纳米酶迅速发展，在设计和应用方面取得了重大进展，特别是通过机器学习（ML）的整合。机器学习通过预测理想的尺寸、形状和表面化学性质，优化了纳米酶的效率，减少了实验时间和资源。本综述探讨了纳米酶技术的快速进展，强调了机器学习在改善各种生物应用性能方面的作用，包括实时监测以及化学发光、电化学和比色传感器的开发。我们讨论了不同类型纳米酶的演变、它们的催化机制以及机器学习对其性能优化的影响。此外，本综述还讨论了与数据质量、可扩展性和标准化相关的挑战，同时强调了机器学习驱动的纳米酶开发的未来方向。通过审视近期的创新成果，本综述突出了将纳米酶与机器学习相结合以推动下一代诊断和检测技术发展的潜力。
