关于革命和障碍:机器学习在生物催化剂中的新兴作用
Tobias Vornholt1,2, Peter Stockinger3,4, Mojmír Mutný5,6
1Department of Chemistry, University of Basel, 4058 Basel, Switzerland.
ACS central science
|October 27, 2025
概括
机器学习 (ML) 通过分析生物数据来加速酶的发展,以发现和设计新的生物催化剂. 这项技术增强了酶工程,克服了当前的采用挑战,以获得更广泛的影响.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 计算生物学和生物信息学
- 合成生物学 合成生物学
背景情况:
- 机器学习 (ML) 正在成为生物催化剂的转型技术.
- 机器学习模型分析复杂的生物数据,包括氨基酸序列,蛋白质结构和功能信息.
- 与DNA合成,测序,自动化和选方面的进步相结合,增强了ML的影响.
研究的目的:
- 审查ML在酶发现,设计和工程中的近期应用.
- 确定ML驱动生物催化物的当前挑战和新兴解决方案.
- 讨论在生物催化剂社区广泛采用ML的障碍.
主要方法:
- 审查最近关于ML在酶发现,设计和工程中的应用文献.
- 分析ML模型在导航蛋白质健身景观中的能力.
- 讨论与实验室自动化和高通量选的整合策略.
主要成果:
- ML促进了复杂的健身景观的导航,以优化酶.
- ML有助于从数据库中发现新的酶,并设计新的生物催化剂.
- ML显著增加了酶开发的速度和效率.
结论:
- 机器学习是一项关键技术,即将彻底改变酶开发.
- 解决采用障碍和促进跨学科合作对于最大限度地发挥ML在生物催化中的潜力至关重要.
- 建议采用合作的最佳实践,以加快ML集成.
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