人工智能驱动的纳米酶设计革命:从偶然性到理性工程
Yixin Yu1,2, Mingzhen Zhang3, Kelong Fan1,2,4
1CAS Engineering Laboratory for Nanozyme, Key Laboratory of Biomacromolecules (CAS), CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. fankelong@ibp.ac.cn.
Materials horizons
|June 26, 2025
概括
机器学习 (ML) 正在通过实现数据驱动设计和智能应用来彻底改变纳米酶研究. 这种方法克服了传统的局限性,加速了各种领域的新型纳米酶的开发.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 计算化学计算化学
背景情况:
- 纳米酶是具有类似酶的催化活性的纳米材料,在诊断,工业和环境修复方面具有潜力.
- 传统的纳米酶开发面临由于性能异质性和复杂的结构-活动关系的挑战,导致效率低下的设计周期.
研究的目的:
- 审查将机器学习 (ML) 整合到纳米酶研究中.
- 为 ML 辅助的纳米酶研究提出一个框架.
- 突出ML在预测活动,优化结构和启用智能应用中的作用.
主要方法:
- 系统地引入与纳米酶研究相关的ML分类和算法.
- 阐明ML的技术优势和拟议的ML辅助研究框架.
- 分析展示在纳米酶开发中的ML应用的案例研究.
主要成果:
- 在纳米酶研发中,ML促进了向数据驱动和理论计算方法的转变.
- ML有助于预测纳米酶催化活性和优化它们的结构.
- ML使得纳米酶的智能应用程序的开发成为可能.
结论:
- 机器学习集成为合理的纳米酶设计和开发提供了一个变革性的范式.
- 解决数据质量和模型可解释性等挑战对于推进ML辅助纳米酶研究至关重要.
- 未来的优化策略将提高纳米酶研究的效率,精度和智能.
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