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深度学习和生成人工智能在酶和细胞工程中的方法

Steffen Docter¹, Benoit David¹, Holger Gohlke²

¹Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.

Current opinion in biotechnology

|December 4, 2025

概括

人工智能 (AI) 加快了有效的酶和微生物系统的创建,以实现可持续的生物经济. 这篇评论涵盖了AI.

科学领域:

生物技术和合成生物学
生物工程是生物工程.
计算生物学计算生物学

背景情况:

可持续的生物经济依赖于高效的酶和微生物工厂.
开发这些生物系统传统上涉及耗时的实验过程.

研究的目的:

审查人工智能 (AI) 在生物催化剂和蜂网络优化方面的进展.
突出 AI 在酶工程和合成生物学中的应用.

主要方法:

关于人工智能在酶发现,工程和新设计中的应用的最新文献的综述.
分析AI在优化细胞组件 (如基因表达和代谢途径) 中的作用.

主要成果:

人工智能显著加快了酶的发展,包括发现,工程和de novo设计.
人工智能成功地优化了细胞功能,包括基因表达,代谢途径和遗传网络.
当前的人工智能方法看起来很有前途,但在可靠性和通用性方面面临挑战.

结论:

人工智能是推动生物催化剂和微生物工厂发展的变革性工具.
需要进一步的研究来克服局限性,并提高人工智能在合成生物学中的稳定性.
人工智能集成对于实现可持续生物经济至关重要.

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