Dept. Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States.
Dept. Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States;, Email:
Chimia (Aarau). 2021 Aug 25;75(7-8):592-597. doi: 10.2533/chimia.2021.592.
Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past it is intrinsically limited and inefficient. To address this problem, our laboratory has developed a fully informatics guided workflow to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and optimization of any class of catalyst for any transformation. This approach is mechanistically agnostic, but also serves as a discovery platform to identify high performing catalysts that can be subsequently investigated with physical organic methods to identify the origins of selectivity.
催化剂的优化对于对映选择性转化一直依赖于对催化剂性质的经验评估。尽管这种方法在过去取得了成功，但它本质上是有限的和低效的。为了解决这个问题，我们的实验室开发了一个完全由信息学指导的工作流程，利用人工智能（AI）和机器学习（ML）的力量来加速任何一类催化剂对于任何转化的发现和优化。这种方法在机理上是不可知的，但也可以作为一个发现平台，以确定高性能的催化剂，然后可以用物理有机方法来研究它们，以确定选择性的起源。
