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Designer substrate library for quantitative, predictive modeling of reaction performance.

ABSTRACT: Assessment of reaction substrate scope is often a qualitative endeavor that provides general indications of substrate sensitivity to a measured reaction outcome. Unfortunately, this field standard typically falls short of enabling the quantitative prediction of new substrates' performance. The disconnection between a reaction's development and the quantitative prediction of new substrates' behavior limits the applicative usefulness of many methodologies. Herein, we present a method by which substrate libraries can be systematically developed to enable quantitative modeling of reaction systems and the prediction of new reaction outcomes. Presented in the context of rhodium-catalyzed asymmetric transfer hydrogenation, these models quantify the molecular features that influence enantioselection and, in so doing, lend mechanistic insight to the modes of asymmetric induction.

SUBMITTER: Bess EN

PROVIDER: S-EPMC4205662 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Designer substrate library for quantitative, predictive modeling of reaction performance.

Bess Elizabeth N EN Bischoff Amanda J AJ Sigman Matthew S MS

Proceedings of the National Academy of Sciences of the United States of America 20140929 41

Assessment of reaction substrate scope is often a qualitative endeavor that provides general indications of substrate sensitivity to a measured reaction outcome. Unfortunately, this field standard typically falls short of enabling the quantitative prediction of new substrates' performance. The disconnection between a reaction's development and the quantitative prediction of new substrates' behavior limits the applicative usefulness of many methodologies. Herein, we present a method by which subs ...[more]

PMID: 25267648

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Dataset Information

Designer substrate library for quantitative, predictive modeling of reaction performance.

Publications

Designer substrate library for quantitative, predictive modeling of reaction performance.

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