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.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.

A dynamic picture of the halolactonization reaction through a combination of ab initio metadynamics and experimental investigations

“…The elucidation of a reaction mechanism does not only improve the fundamental understanding of chemical transformations, it also aids in future catalyst design. 43,44 State-of-theart research, oen uses an integrated computational and experimental approach to gain such knowledge, as exemplied by the collaborative works of Jackson and Borhan. [37][38][39] On the computational side, static DFT calculations are routinely used, due to the availability of several user friendly soware, limited hardware requirements and the extensive development of this approach.…”

Application of Coordination Compounds with Transition Metal Ions in the Chemical Industry—A Review

“…In the alkenic hydrocyanation process, the use of bident phosphorus-based ligands is of great importance, which contributes to increasing the stability of the products obtained during hydrocyanation [ 40 ]. In addition to nickel complexes, ruthenium(I) complexes for hydrocyanation are used [ 41 , 42 , 43 ].…”

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“…The buried volumes for each quadrant and the whole molecule were calculated as summarized in Figure 3c. The correlation between enantioselectivity and the steric descriptor was analyzed to establish the quantitative structureselectivity relationship [23] (QSSR). Ap ronouncedl inear relationship (R 2 = 0.95) was observed between log (er) (er = enantiomeric ratio) and %V bur of the southeastern quadrant.…”