Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid–catalyzed thiol addition to N-acylimines.

The existence of the oft-invoked intermediates containing the crucial Pd-O-B subunit, the "missing link", has been established in the Suzuki-Miyaura cross-coupling reaction. The use of low-temperature, rapid injection NMR spectroscopy (RI-NMR), kinetic studies, and computational analysis has enabled the generation, observation, and characterization of these highly elusive species. The ability to confirm the intermediacy of Pd-O-B-containing species provided the opportunity to clarify mechanistic aspects of the transfer of the organic moiety from boron to palladium in the key transmetalation step. Specifically, these studies establish the identity of two different intermediates containing Pd-O-B linkages, a tri-coordinate (6-B-3) boronic acid complex and a tetra-coordinate (8-B-4) boronate complex, both of which undergo transmetalation leading to the cross-coupling product. Two distinct mechanistic pathways have been elucidated for stoichiometric reactions of these complexes: (1) transmetalation via an unactivated 6-B-3 intermediate that dominates in the presence of an excess of ligand, and (2) transmetalation via an activated 8-B-4 intermediate that takes place with a deficiency of ligand.

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.

The Suzuki–Miyaura reaction is the most practiced palladium-catalyzed, cross-coupling reaction because of its broad applicability, low toxicity of the metal (B), and the wide variety of commercially available boron substrates. A wide variety of boronic acids and esters, each with different properties, have been developed for this process. Despite the popularity of the Suzuki–Miyaura reaction, the precise manner in which the organic fragment is transferred from boron to palladium has remained elusive for these reagents. Herein, we report the observation and characterization of pretransmetalation intermediates generated from a variety of commonly employed boronic esters. The ability to confirm the intermediacy of pretransmetalation intermediates provided the opportunity to clarify mechanistic aspects of the transfer of the organic moiety from boron to palladium in the key transmetalation step. A series of structural, kinetic, and computational investigations revealed that boronic esters can transmetalate directly without prior hydrolysis. Furthermore, depending on the boronic ester employed, significant rate enhancements for the transfer of the B-aryl groups were observed. Overall, two critical features were identified that enable the transfer of the organic fragment from boron to palladium: (1) the ability to create an empty coordination site on the palladium atom and (2) the nucleophilic character of the ipso carbon bound to boron. Both of these features ultimately relate to the electron density of the oxygen atoms in the boronic ester.

Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.

Conspectus Catalyst design in enantioselective catalysis has historically been driven by empiricism. In this endeavor, experimentalists attempt to qualitatively identify trends in structure that lead to a desired catalyst function. In this body of work, we lay the groundwork for an improved, alternative workflow that uses quantitative methods to inform decision making at every step of the process. At the outset, we define a library of synthetically accessible permutations of a catalyst scaffold with the philosophy that the library contains every potential catalyst we are willing to make. To represent these chiral molecules, we have developed general 3D representations, which can be calculated for tens of thousands of structures. This defines the total chemical space of a given catalyst scaffold; it is constructed on the basis of catalyst structure only without regard to a specific reaction or mechanism. As such, any algorithmic subset selection method, which is unsupervised (i.e., only considers catalyst structure), should provide an ideal initial screening set for any new reaction that can be catalyzed by that scaffold. Notably, because this design strategy, the same set of catalysts can be used for any reaction that can be catalyzed with that parent catalyst scaffold. These are tested experimentally, and statistical learning tools can be used to create a model relating catalyst structure to catalyst function. Further, this model can be used to predict the performance of each catalyst candidate in the greater database of virtual catalyst candidates. In this way, it is possible estimate the performance of tens of thousands of catalysts by experimentally testing a smaller subset. Using error assessment metrics, it is possible to understand the confidence in new predictions. An experimentalist using this tool can balance the predicted results (reward) with the prediction confidence (risk) when deciding which catalysts to synthesize next in an optimization campaign. These catalysts are synthesized and tested experimentally. At this stage, either the optimization is a success or the predicted values were incorrect and further optimization is required. In the case of the latter, the information can be fed back into the statistical learning model to refine the model, and this iterative process can be used to determine the optimal catalyst. In this body of work, we not only establish this workflow but quantitatively establish how best to execute each step. Herein, we evaluate several 3D molecular representations to determine how best to represent molecules. Several selection protocols are examined to best decide which set of molecules can be used to represent the library of interest. In addition, the number of reactions needed to make accurate, statistical learning models is evaluated. Taken together these components establish a tool ready to progress from the development stage to the utility stage. As such, current research endeavors focus on applying these tools to optimize new reactions.

Regression modeling is becoming increasingly prevalent in organic chemistry as a tool for reaction outcome prediction and mechanistic interrogation. Frequently, to acquire the requisite amount of data for such studies, researchers employ combinatorial datasets to maximize the number of data points while limiting the number of discrete chemical entities required. An often-overlooked problem in modeling studies using combinatorial datasets is the tendency to fit on patterns in the datasets (i.e., the presence or absence of a reactant or catalyst) rather than to identify meaningful trends between descriptors and the response variable. Consequently, the generality and interpretability of such models suffer. This report illustrates these well-known pitfalls in a case study, demonstrates the necessary control experiments to identify when this property will be problematic, and suggests how to perform further validation to assess general applicability and interpretability of models trained using combinatorial datasets.

Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from in silico libraries of catalyst structures.