Screening for generality in asymmetric catalysis

Wagen, Corin; McMinn, Spencer E.; Kwan, Eugene E.; Jacobsen, Eric N.

doi:10.1038/s41586-022-05263-2

Cited by 39 publications

(48 citation statements)

References 45 publications

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“…with few examples in transition metal-, , bio-, and photocatalysis . Regarding asymmetric catalysis, recent works have focused on using high-throughput techniques allowing for direct comparative studies of catalyst performance. , While such protocols assess an important aspect of generality, they do not capture the impact of the catalyst structure in a high-dimensional search space. Importantly, even in these situations where relevant data are supplied, it may not always be clear which catalyst is the most general and, in some cases, the most widely applicable catalyst may not be included in the original screening set.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

et al. 2023

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The development of chiral catalysts that can provide high enantioselectivities across a wide assortment of substrates or reaction range is a priority for many catalyst design efforts. While several approaches are available to aid in the identification of general catalyst systems, there is currently no simple procedure for directly measuring how general a given catalyst could be. Herein, we present a catalyst-agnostic workflow centered on unsupervised machine learning that enables the rapid assessment and quantification of catalyst generality. The workflow uses curated literature data sets and reaction descriptors to visualize and cluster chemical space coverage. This reaction network can then be applied to derive a catalyst generality metric through designer equations and interfaced with other regression techniques for general catalyst prediction. As validating case studies, we have successfully applied this method to identify-through-quantification the most general catalyst chemotype for an organocatalytic asymmetric Mannich reaction and predicted the most general chiral phosphoric acid catalyst for the addition of nucleophiles to imines. The mechanistic basis for catalyst generality can then be gleaned from the calculated values by deconstructing the contributions of chemical space and enantiomeric excess to the overall result. Finally, our generality techniques permitted the development of mechanistically informative catalyst screening sets that allow experimentalists to rationally select catalysts that have the highest probability of achieving a good result in the first round of reaction development. Overall, our findings represent a framework for interrogating catalyst generality, and this strategy should be relevant to other catalytic systems widely applied in asymmetric synthesis.

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Section: Introductionmentioning

confidence: 99%

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

et al. 2023

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“…Even so, specificity-oriented optimization has been broadly adopted in the field, resulting in many catalysts that are tailored for one model substrate but often exhibit diminished selectivity upon surveying a diverse substrate scope (Fig. 1A) ( 4 ). To address the limited transferability frequently encountered in asymmetric catalysis, we envisioned an optimization strategy with generality as the primary objective in catalyst development.…”

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confidence: 99%

“…Recently, Jacobsen and co-workers reported a pooled-product method for enantioselectivity analysis, which enabled high-throughput evaluation of the generality of known catalysts in the context of an asymmetric Pictet-Spengler reaction ( 4 ). Our interest focused on advancing the notion of enhanced substrate breadth in the context of previously elusive transformations by using a new catalytic platform.…”

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confidence: 99%

Generality-oriented optimization of enantioselective aminoxyl radical catalysis

Rein

Rozema

Langner

et al. 2023

Science

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Catalytic enantioselective methods that are generally applicable to a broad range of substrates are rare. We report a strategy for the oxidative desymmetrization of meso -diols predicated on a nontraditional catalyst optimization protocol by using a panel of screening substrates rather than a singular model substrate. Critical to this approach was rational modulation of a peptide sequence in the catalyst incorporating a distinct aminoxyl-based active residue. A general catalyst emerged, providing high selectivity in the delivery of enantioenriched lactones across a broad range of diols, while also achieving up to ~100,000 turnovers.

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“…9 Regarding asymmetric catalysis, recent works have focused on using high-throughput techniques allowing for direct comparative studies of catalyst performance. 10,11 While such protocols assess an important aspect of generality, they do not capture the impact of the catalyst structure in high-dimensional search space. Importantly, even in these situations where relevant data is supplied, it may not always be clear which catalyst is the most general and, in some cases, the most widely applicable catalyst may not be included in the original screening set.…”

mentioning

confidence: 99%

An Unsupervised Machine Learning Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

Betinol

Lai

Thakur

et al. 2023

Preprint

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The development of chiral catalysts that can provide high enantioselectivities across a wide assortment of substrates or reaction range is a priority for many catalyst design efforts. While several approaches are available to aid in the identification of general catalyst systems there is currently no simple procedure for directly measuring how general a given catalyst could be. Herein, we present a catalyst-agnostic workflow centered on unsupervised machine learning that enables the rapid assessment and quantification of catalyst generality. The workflow uses curated literature data sets and reaction descriptors to visualize and cluster chemical space coverage. This reaction network can then be applied to derive a catalyst generality metric through designer equations and interfaced with other regression techniques for general catalyst prediction. As validating case studies, we have successfully applied this method to identi-fy-through-quantification the most general catalyst chemotype for an organocatalytic asymmetric Mannich reaction and predicted the most general chiral phosphoric acid catalyst for the addition of nucleophiles to imines. The mechanistic basis for catalyst generality can then be gleaned from the calculated values by deconstructing the contributions of chemical space and enantiomeric excess to the overall result. We conclude that broadly applicable catalysts may be more adaptative to changes in reactant structure because enantioinduction does not rely on a single set of noncovalent interactions. In contrast, some systems work by engaging in robust noncovalent contacts that do not change significantly in nature when the structure of the reaction component is altered. Finally, our generality techniques permit-ted the development of mechanistically informative catalyst screening sets that allow experimentalists to rationally select catalysts that have the highest probability of achieving a good result in the first round of reaction development. Overall, our findings represent a framework for interrogating and predicting catalyst generality, and this strategy should be relevant to other catalytic systems widely applied in asymmetric synthesis.

show abstract

Screening for generality in asymmetric catalysis

Cited by 39 publications

References 45 publications

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

Generality-oriented optimization of enantioselective aminoxyl radical catalysis

An Unsupervised Machine Learning Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

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