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DFT/NMR Approach for the Configuration Assignment of Groups of Stereoisomers by the Combination and Comparison of Experimental and Predicted Sets of Data
[Image: see text] Quantum mechanical/nuclear magnetic resonance (NMR) approaches are widely used for the configuration assignment of organic compounds generally comparing one cluster of experimentally determined data (e.g., (13)C NMR chemical shifts) with those predicted for all possible theoretical...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997581/ https://www.ncbi.nlm.nih.gov/pubmed/31961156 http://dx.doi.org/10.1021/acs.joc.9b03129 |
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author | Lauro, Gianluigi Das, Pronay Riccio, Raffaele Reddy, D. Srinivasa Bifulco, Giuseppe |
author_facet | Lauro, Gianluigi Das, Pronay Riccio, Raffaele Reddy, D. Srinivasa Bifulco, Giuseppe |
author_sort | Lauro, Gianluigi |
collection | PubMed |
description | [Image: see text] Quantum mechanical/nuclear magnetic resonance (NMR) approaches are widely used for the configuration assignment of organic compounds generally comparing one cluster of experimentally determined data (e.g., (13)C NMR chemical shifts) with those predicted for all possible theoretical stereoisomers. More than one set of experimental data, each related to a specific stereoisomer, may occur in some cases, and the accurate stereoassignments can be obtained by combining the experimental and computed data. We introduce here a straightforward methodology based on the simultaneous analysis, combination, and comparison of all sets of experimental/calculated (13)C chemical shifts for aiding the correct configuration assignment of groups of stereoisomers. The comparison of the differences between the calculated/experimental chemical shifts instead of the shifts themselves led to the advantage of avoiding errors arising from calibration procedures, reducing systematic errors, and highlighting the most diagnostic differences between calculated and experimental data. This methodology was applied on a tetrad of synthesized cladosporin stereoisomers (cladologs) and further corroborated on a tetrad of pochonicine stereoisomers, obtaining the correct correspondences between experimental and calculated sets of data. The new MAE(ΔΔδ) parameter, useful for indicating the best fit between sets of experimental and calculated data, is here introduced for facilitating the stereochemical assignment of groups of stereoisomers. |
format | Online Article Text |
id | pubmed-7997581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-79975812021-03-29 DFT/NMR Approach for the Configuration Assignment of Groups of Stereoisomers by the Combination and Comparison of Experimental and Predicted Sets of Data Lauro, Gianluigi Das, Pronay Riccio, Raffaele Reddy, D. Srinivasa Bifulco, Giuseppe J Org Chem [Image: see text] Quantum mechanical/nuclear magnetic resonance (NMR) approaches are widely used for the configuration assignment of organic compounds generally comparing one cluster of experimentally determined data (e.g., (13)C NMR chemical shifts) with those predicted for all possible theoretical stereoisomers. More than one set of experimental data, each related to a specific stereoisomer, may occur in some cases, and the accurate stereoassignments can be obtained by combining the experimental and computed data. We introduce here a straightforward methodology based on the simultaneous analysis, combination, and comparison of all sets of experimental/calculated (13)C chemical shifts for aiding the correct configuration assignment of groups of stereoisomers. The comparison of the differences between the calculated/experimental chemical shifts instead of the shifts themselves led to the advantage of avoiding errors arising from calibration procedures, reducing systematic errors, and highlighting the most diagnostic differences between calculated and experimental data. This methodology was applied on a tetrad of synthesized cladosporin stereoisomers (cladologs) and further corroborated on a tetrad of pochonicine stereoisomers, obtaining the correct correspondences between experimental and calculated sets of data. The new MAE(ΔΔδ) parameter, useful for indicating the best fit between sets of experimental and calculated data, is here introduced for facilitating the stereochemical assignment of groups of stereoisomers. American Chemical Society 2020-01-21 2020-03-06 /pmc/articles/PMC7997581/ /pubmed/31961156 http://dx.doi.org/10.1021/acs.joc.9b03129 Text en Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Lauro, Gianluigi Das, Pronay Riccio, Raffaele Reddy, D. Srinivasa Bifulco, Giuseppe DFT/NMR Approach for the Configuration Assignment of Groups of Stereoisomers by the Combination and Comparison of Experimental and Predicted Sets of Data |
title | DFT/NMR Approach for
the Configuration Assignment
of Groups of Stereoisomers by the Combination and Comparison of Experimental
and Predicted Sets of Data |
title_full | DFT/NMR Approach for
the Configuration Assignment
of Groups of Stereoisomers by the Combination and Comparison of Experimental
and Predicted Sets of Data |
title_fullStr | DFT/NMR Approach for
the Configuration Assignment
of Groups of Stereoisomers by the Combination and Comparison of Experimental
and Predicted Sets of Data |
title_full_unstemmed | DFT/NMR Approach for
the Configuration Assignment
of Groups of Stereoisomers by the Combination and Comparison of Experimental
and Predicted Sets of Data |
title_short | DFT/NMR Approach for
the Configuration Assignment
of Groups of Stereoisomers by the Combination and Comparison of Experimental
and Predicted Sets of Data |
title_sort | dft/nmr approach for
the configuration assignment
of groups of stereoisomers by the combination and comparison of experimental
and predicted sets of data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997581/ https://www.ncbi.nlm.nih.gov/pubmed/31961156 http://dx.doi.org/10.1021/acs.joc.9b03129 |
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