预测生物催化中的机器学习:方法和应用的比较审查
Neha Tripathi1, Joan Hérisson1, Jean-Loup Faulon2
1Genomics Metabolics, Genoscope, François Jacob Institute, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057 Evry, France.
Biotechnology advances
|August 30, 2025
概括
机器学习促进了酶发现和过程优化的预测生物催化. 本综述分析了用于开发可持续生物催化应用的计算工具和数据集成.
科学领域:
- 生物技术和生物化学
- 计算生物学
- 酶工程
背景情况:
- 机器学习 (ML) 已经彻底改变了预测生物催化剂.
- 目前的ML方法可以预测酶功能,发现和反应建模.
研究的目的:
- 提供预测生物催化剂中的ML方法的比较分析.
- 突出计算工具和生物化学数据之间的协同作用.
- 讨论酶分类,反应注释和动力参数预测方面的进展.
主要方法:
- 审查各种ML方法,包括深度神经网络,卷积网络,基于图形的架构和变压器.
- 数据整合,表示和特色化技术的分析.
- 对生物催化剂ML模型的验证方法的检查.
主要成果:
- ML加速了酶的发现和可持续的生物催化工艺的发展.
- 不同的ML架构为特定的生物催化任务提供了独特的优势和局限性.
- 整合大规模数据和可靠的验证对于成功至关重要.
结论:
- 机器学习对于将计算洞察与酶工程联系起来至关重要.
- 未来的应用包括合成生物学,代谢工程和绿色生物催化.
- 机械制造技术的不断进步将推动生物催化技术的创新.
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