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Prediction of Stereochemistry using Q2MM

[Image: see text] The standard method of screening ligands for selectivity in asymmetric, transition metal-catalyzed reactions requires experimental testing of hundreds of ligands from ligand libraries. This “trial and error” process is costly in terms of time as well as resources and, in general, i...

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Autores principales: Hansen, Eric, Rosales, Anthony R., Tutkowski, Brandon, Norrby, Per-Ola, Wiest, Olaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2016
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879660/
https://www.ncbi.nlm.nih.gov/pubmed/27064579
http://dx.doi.org/10.1021/acs.accounts.6b00037
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author Hansen, Eric
Rosales, Anthony R.
Tutkowski, Brandon
Norrby, Per-Ola
Wiest, Olaf
author_facet Hansen, Eric
Rosales, Anthony R.
Tutkowski, Brandon
Norrby, Per-Ola
Wiest, Olaf
author_sort Hansen, Eric
collection PubMed
description [Image: see text] The standard method of screening ligands for selectivity in asymmetric, transition metal-catalyzed reactions requires experimental testing of hundreds of ligands from ligand libraries. This “trial and error” process is costly in terms of time as well as resources and, in general, is scientifically and intellectually unsatisfying as it reveals little about the underlying mechanism behind the selectivity. The accurate computational prediction of stereoselectivity in enantioselective catalysis requires adequate conformational sampling of the selectivity-determining transition state but has to be fast enough to compete with experimental screening techniques to be useful for the synthetic chemist. Although electronic structure calculations are accurate and general, they are too slow to allow for sampling or fast screening of ligand libraries. The combined requirements can be fulfilled by using appropriately fitted transition state force fields (TSFFs) that represent the transition state as a minimum and allow fast conformational sampling using Monte Carlo. Quantum-guided molecular mechanics (Q2MM) is an automated force field parametrization method that generates accurate, reaction-specific TSFFs by fitting the functional form of an arbitrary force field using only electronic structure calculations by minimization of an objective function. A key feature that distinguishes the Q2MM method from many other automated parametrization procedures is the use of the Hessian matrix in addition to geometric parameters and relative energies. This alleviates the known problems of overfitting of TSFFs. After validation of the TSFF by comparison to electronic structure results for a test set and available experimental data, the stereoselectivity of a reaction can be calculated by summation over the Boltzman-averaged relative energies of the conformations leading to the different stereoisomers. The Q2MM method has been applied successfully to perform virtual ligand screens on a range of transition metal-catalyzed reactions that are important from both an industrial and an academic perspective. In this Account, we provide an overview of the continued improvement of the prediction of stereochemistry using Q2MM-derived TSFFs using four examples from different stages of development: (i) Pd-catalyzed allylation, (ii) OsO(4)-catalyzed asymmetric dihydroxylation (AD) of alkenes, (iii) Rh-catalyzed hydrogenation of enamides, and (iv) Ru-catalyzed hydrogenation of ketones. In the current form, correlation coefficients of 0.8–0.9 between calculated and experimental ee values are typical for a wide range of substrate–ligand combinations, and suitable ligands can be predicted for a given substrate with ∼80% accuracy. Although the generation of a TSFF requires an initial effort and will therefore be most useful for widely used reactions that require frequent screening campaigns, the method allows for a rapid virtual screen of large ligand libraries to focus experimental efforts on the most promising substrate–ligand combinations.
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spelling pubmed-48796602016-05-27 Prediction of Stereochemistry using Q2MM Hansen, Eric Rosales, Anthony R. Tutkowski, Brandon Norrby, Per-Ola Wiest, Olaf Acc Chem Res [Image: see text] The standard method of screening ligands for selectivity in asymmetric, transition metal-catalyzed reactions requires experimental testing of hundreds of ligands from ligand libraries. This “trial and error” process is costly in terms of time as well as resources and, in general, is scientifically and intellectually unsatisfying as it reveals little about the underlying mechanism behind the selectivity. The accurate computational prediction of stereoselectivity in enantioselective catalysis requires adequate conformational sampling of the selectivity-determining transition state but has to be fast enough to compete with experimental screening techniques to be useful for the synthetic chemist. Although electronic structure calculations are accurate and general, they are too slow to allow for sampling or fast screening of ligand libraries. The combined requirements can be fulfilled by using appropriately fitted transition state force fields (TSFFs) that represent the transition state as a minimum and allow fast conformational sampling using Monte Carlo. Quantum-guided molecular mechanics (Q2MM) is an automated force field parametrization method that generates accurate, reaction-specific TSFFs by fitting the functional form of an arbitrary force field using only electronic structure calculations by minimization of an objective function. A key feature that distinguishes the Q2MM method from many other automated parametrization procedures is the use of the Hessian matrix in addition to geometric parameters and relative energies. This alleviates the known problems of overfitting of TSFFs. After validation of the TSFF by comparison to electronic structure results for a test set and available experimental data, the stereoselectivity of a reaction can be calculated by summation over the Boltzman-averaged relative energies of the conformations leading to the different stereoisomers. The Q2MM method has been applied successfully to perform virtual ligand screens on a range of transition metal-catalyzed reactions that are important from both an industrial and an academic perspective. In this Account, we provide an overview of the continued improvement of the prediction of stereochemistry using Q2MM-derived TSFFs using four examples from different stages of development: (i) Pd-catalyzed allylation, (ii) OsO(4)-catalyzed asymmetric dihydroxylation (AD) of alkenes, (iii) Rh-catalyzed hydrogenation of enamides, and (iv) Ru-catalyzed hydrogenation of ketones. In the current form, correlation coefficients of 0.8–0.9 between calculated and experimental ee values are typical for a wide range of substrate–ligand combinations, and suitable ligands can be predicted for a given substrate with ∼80% accuracy. Although the generation of a TSFF requires an initial effort and will therefore be most useful for widely used reactions that require frequent screening campaigns, the method allows for a rapid virtual screen of large ligand libraries to focus experimental efforts on the most promising substrate–ligand combinations. American Chemical Society 2016-04-11 2016-05-17 /pmc/articles/PMC4879660/ /pubmed/27064579 http://dx.doi.org/10.1021/acs.accounts.6b00037 Text en Copyright © 2016 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Hansen, Eric
Rosales, Anthony R.
Tutkowski, Brandon
Norrby, Per-Ola
Wiest, Olaf
Prediction of Stereochemistry using Q2MM
title Prediction of Stereochemistry using Q2MM
title_full Prediction of Stereochemistry using Q2MM
title_fullStr Prediction of Stereochemistry using Q2MM
title_full_unstemmed Prediction of Stereochemistry using Q2MM
title_short Prediction of Stereochemistry using Q2MM
title_sort prediction of stereochemistry using q2mm
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879660/
https://www.ncbi.nlm.nih.gov/pubmed/27064579
http://dx.doi.org/10.1021/acs.accounts.6b00037
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