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WIREs Comput Mol Sci
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Selectivity in organocatalysis—From qualitative to quantitative predictive models

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Abstract Recent advances in both experimental and computational techniques pose an exciting time for chemistry. Computational tools traditionally used to interpret experimental trends have now evolved into predictive models able to guide the design of novel catalysts. This review discusses the evolution of these models, as well as challenges and future avenues in the field of organocatalysis. Through representative examples we demonstrate how traditional physical organic chemistry tools in combination with machine learning models provide a powerful approach to achieve deeper understanding alongside greater predictive power. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Electronic Structure Theory > Density Functional Theory Data Science > Artificial Intelligence/Machine Learning
Synergistic relationship between the fields of organocatalysis and computational/kinetic analysis leading to the development of predictive models by qualitative and quantitative approaches. This relationship is exemplified by the Houk‐List model for the proline‐catalyzed Aldol reaction of carbonyl compounds. QM, quantum mechanics; LFER, linear free energy relationship; ML, machine learning
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Examples of the ML models and their respective applications in chemistry. (a) Prediction of enantioselectivity for CPA‐catalyzed additions to imines.62 (b) Prediction of enantioselectivity for the addition of thiols to N‐acyl imines.63 (c) Prediction of regioselectivity for difluorination reactions catalyzed by hypervalent iodine.65 (d) Prediction of regioselectivity in radical C−H functionalization.66 CPA, chiral phosphoric acid; ML, machine learning
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(a) Workflow for obtaining quantitative models for organocatalytic processes. (b) List's IDPi‐catalyzed Diels‐Alder reaction studied by Bistoni and coworkers46 Tf = SO2CF3. (c) Goodman's model for the prediction of the favored TS in 1,1′‐Bi‐2‐naphthol (BINOL)‐derived catalytic reactions.48 (d) Wei and Lan's model for an N‐heterocyclic carbene (NHC)‐catalyzed imine condensation using electrophilicity (ω) and nucleophilicity (N) parameters.49
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Qualitative models for organocatalytic selectivity based on the Klopman–Salem equation: Macmillan's (a) and Ishihara's (b) models for Diels‐Alder reactions. (c) Mode of action of proline‐derived organocatalysts developed by Hayashi and Jørgensen, and application in a 1,3‐dipolar cycloaddition by Merino and Vicario (d)
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Relationship between selectivity (ΔΔG(‡)/kcal mol−1) and the observed ratio of products (n:1) for two competing reactions at 25°C (blue line). Values in green show the span in ΔΔG(‡) to overturn selectivity from 99% to −99% ee (dashed lines). Solid black line and gray shaded region show the range in ee with an error in ΔΔG(‡) of ±1 kcal mol−1 for an observed ee of 70%
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(a) The four types of selectivity encountered in organocatalysis. (b) Schematic representation of kinetic and thermodynamic control. ki is the rate of formation of the ith species, K is the equilibrium constant, ΔΔG and ΔΔGr are the difference in the free energy of the lowest energy transition states and products, respectively, R is the gas constant, and T is the temperature. P, product; SM, starting material
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Computer and Information Science > Visualization
Electronic Structure Theory > Density Functional Theory
Structure and Mechanism > Reaction Mechanisms and Catalysis

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