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.1644 3.3. Perspective on CCM 1649 4. Chirality Codes 1649 4.1. Introduction to Chirality Codes 1649 4.2. Application of CICC 1649 4.3. Other Chirality Codes 1654 4.4. Conclusion and Perspective 1654 5. Molecular Interaction Field (MIF) Based Methods 1655 5.1. Alignment Dependent MIF Methods 1655 5.1.1. Background to Alignment Dependent MIF Methods 1655 5.1.2. Applications of Alignment Dependent MIF-Based Methods in Asymmetric Catalysis 1656 5.1.3.

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Cellular RNA labeling strategies based on bioorthogonal chemical reactions are much less developed in comparison to glycan, protein and DNA due to its inherent instability and lack of effective methods to introduce bioorthogonal reactive functionalities (e.g. azide) into RNA. Here we report the development of a simple and modular posttranscriptional chemical labeling and imaging technique for RNA by using a novel toolbox comprised of azide-modified UTP analogs. These analogs facilitate the enzymatic incorporation of azide groups into RNA, which can be posttranscriptionally labeled with a variety of probes by click and Staudinger reactions. Importantly, we show for the first time the specific incorporation of azide groups into cellular RNA by endogenous RNA polymerases, which enabled the imaging of newly transcribing RNA in fixed and in live cells by click reactions. This labeling method is practical and provides a new platform to study RNA in vitro and in cells.

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