Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future
Abstract
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.
Additional Information
© 2019 American Chemical Society. Received: July 2, 2019; Published: December 30, 2019. We are grateful to the National Science Foundation for generous financial support (NSF CHE1900617). We thank Kenny Lipkowitz for providing Figure 27. We thank Jeremy Henle for useful discussion and providing Figure 54. A.F.Z. and S.V.A. thank the University of Illinois for graduate fellowships. Author Contributions: A.F.Z. compiled the reference list and drafted the introduction, linear free energy relationships, continuous chirality measures, chirality codes, molecular interaction fields, miscellaneous, and perspectives sections. S.V.A. provided critical review and commentary, contributed to creating final drafts of every section, including major revisions of the linear free energy relationships section, and drafted the final version of most figures and the abstract. S.E.D. provided financial support, critical review and commentary, and edited text and graphics of the final version. The authors declare no competing financial interest.Attached Files
Supplemental Material - cr9b00425_si_001.pdf
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Additional details
- Eprint ID
- 100456
- Resolver ID
- CaltechAUTHORS:20200102-105123656
- CHE-1900617
- NSF
- University of Illinois
- Created
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2020-01-02Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field