Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate-catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.

中文翻译：

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用于不对称催化的统一机器学习协议，作为使用不对称氢化的概念证明。

不对称催化剂的设计通常涉及时间和资源密集的启发式努力。鉴于近年来对有效催化不对称反应的兴趣稳步增长以及机器学习（ML）领域的快速增长，我们设想将这两个重要领域结合起来。我们从五个不同的不对称联萘衍生的催化剂族中选择了一组量子化学衍生的分子描述子，它们倾向于影响烯烃和亚胺不对称氢化的对映选择性。发现使用一组368种底物-催化剂组合的分子参数建立的随机森林（RF）的预测能力令人印象深刻，预测对映体过量（％ee）的均方根误差（rmse）约8.4±1。8与实验已知值相比。发现RF的准确性优于其他ML方法，例如卷积神经网络，决策树，极限梯度提升以及逐步线性回归。所提出的方法有望为不对称转化催化剂的设计提供飞跃。