通过机器学习加速生物催化发现:酶工程,发现和设计中的范式转变
Braun Markus1, Gruber Christian C2, Krassnigg Andreas2
1Department of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria.
概括
包括人工智能和机器学习在内的计算工具正在彻底改变生物催化剂的酶工程. 这些进步加速了用于制药应用的酶的优化,提供了更快,更准确的解决方案.
科学领域:
- 生物催化剂是一种生物催化剂.
- 蛋白质工程是指蛋白质工程.
- 计算生物学 计算生物学
背景情况:
- 十多年来,预测算法帮助了蛋白质序列空间导航.
- 最近计算工具的进步刺激了酶识别,表征和工程学的快速进步.
- 人工智能 (AI) 和机器学习 (ML) 现在是现代蛋白质科学的组成部分.
研究的目的:
- 审查目前用于生物催化剂的制药行业的计算工具的应用.
- 突出创新的方法和蛋白质工程的最新发展.
- 讨论未来的机会,并批判性地评估AI/ML在生物催化剂中的局限性.
主要方法:
- 对当前文献和行业趋势进行基于视角的审查.
- 对计算蛋白质工程的最新发展进行分析.
- 对生物催化剂中的AI和ML技术进行批判性评估.
主要成果:
- 计算工具,特别是AI和ML,正在显著加速酶工程.
- 这些技术提高了酶开发的速度,准确性和成本效益.
- 制药行业越来越多地采用这些先进的计算方法.
结论:
- 人工智能和机器学习正在改变生物催化剂和蛋白质工程.
- 需要进一步的研究来解决当前的局限性和挑战.
- 在开发高效生物催化剂方面,AI/ML的潜力是巨大的.
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