The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

中文翻译：

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对映选择性催化中的定量结构-选择性关系：过去，现在和将来。

21世纪的曙光带来了与计算机指导的催化剂设计方法相关的大量研究。在过去的二十年中，化学信息学（信息学在解决化学问题中的应用）越来越影响到活性的预测和有机反应的机理研究。先进的统计和机器学习方法的出现以及计算速度和内存的急剧增加，为这一新兴的研究领域做出了贡献。这篇综述总结了在不对称催化反应中采用定量结构-选择性关系（QSSR）的策略。最初通过介绍这些方法的基本功能来构成覆盖范围。根据日益增加的分子表示的复杂性，讨论了后续主题。作为QSSR在对映选择性催化中应用最广泛的子领域，首先描述了局部参数化方法和线性自由能关系（LFER）以及多元建模技术的应用。本节后面是全局参数化方法的描述，第一种方法是连续手性测度（CCM），因为它是衍生自分子全局结构的单个参数。然后，引入手性代码，全局多元变量描述符，然后引入分子相互作用域（MIF），该分子相互作用域通常是具有最高维数的全局描述符类。为了突出目前在对映选择性转化中QSSR的应用范围，我们提供了一个完整的示例集合。当与传统的实验方法结合使用时，