The development of chiral catalysts that can provide high enantioselectivities across a wide assortment of substrates or reaction range is a priority for many catalyst design efforts. While several approaches are available to aid in the identification of general catalyst systems, there is currently no simple procedure for directly measuring how general a given catalyst could be. Herein, we present a catalyst-agnostic workflow centered on unsupervised machine learning that enables the rapid assessment and quantification of catalyst generality. The workflow uses curated literature data sets and reaction descriptors to visualize and cluster chemical space coverage. This reaction network can then be applied to derive a catalyst generality metric through designer equations and interfaced with other regression techniques for general catalyst prediction. As validating case studies, we have successfully applied this method to identify-through-quantification the most general catalyst chemotype for an organocatalytic asymmetric Mannich reaction and predicted the most general chiral phosphoric acid catalyst for the addition of nucleophiles to imines. The mechanistic basis for catalyst generality can then be gleaned from the calculated values by deconstructing the contributions of chemical space and enantiomeric excess to the overall result. Finally, our generality techniques permitted the development of mechanistically informative catalyst screening sets that allow experimentalists to rationally select catalysts that have the highest probability of achieving a good result in the first round of reaction development. Overall, our findings represent a framework for interrogating catalyst generality, and this strategy should be relevant to other catalytic systems widely applied in asymmetric synthesis.

中文翻译：

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用于分配和预测不对称催化一般性的数据驱动工作流程

开发可在各种底物或反应范围内提供高对映选择性的手性催化剂是许多催化剂设计工作的优先事项。虽然有几种方法可用于帮助识别通用催化剂系统，但目前没有简单的程序可以直接测量给定催化剂的通用性。在此，我们提出了一种以无监督机器学习为中心的与催化剂无关的工作流程，该工作流程能够快速评估和量化催化剂的普遍性。该工作流使用精选的文献数据集和反应描述符来可视化和聚类化学空间覆盖。然后可以应用该反应网络通过设计者方程推导催化剂通用性指标，并与其他回归技术相结合以进行一般催化剂预测。作为验证案例研究，我们已成功应用此方法通过量化识别有机催化不对称曼尼希反应的最常见催化剂化学类型，并预测最常见的手性磷酸催化剂用于将亲核试剂加成亚胺。然后可以通过解构化学空间和对映体过量对总体结果的贡献，从计算值中收集催化剂普遍性的机械基础。最后，我们的通用技术允许开发机械信息催化剂筛选集，使实验者能够合理选择在第一轮反应开发中最有可能取得良好结果的催化剂。总的来说，我们的发现代表了一个询问催化剂普遍性的框架，