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Bayesian probabilistic assignment of chemical shifts in organic solids
A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional correlation experiments. An alternative sol...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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American Association for the Advancement of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626066/ https://www.ncbi.nlm.nih.gov/pubmed/34826232 http://dx.doi.org/10.1126/sciadv.abk2341 |
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author | Cordova, Manuel Balodis, Martins Simões de Almeida, Bruno Ceriotti, Michele Emsley, Lyndon |
author_facet | Cordova, Manuel Balodis, Martins Simões de Almeida, Bruno Ceriotti, Michele Emsley, Lyndon |
author_sort | Cordova, Manuel |
collection | PubMed |
description | A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge Structural Database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for more than 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the (13)C and (1)H assignment of 11 molecular solids with experimental shifts and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in more than 80% of cases. |
format | Online Article Text |
id | pubmed-8626066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-86260662021-12-06 Bayesian probabilistic assignment of chemical shifts in organic solids Cordova, Manuel Balodis, Martins Simões de Almeida, Bruno Ceriotti, Michele Emsley, Lyndon Sci Adv Physical and Materials Sciences A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge Structural Database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for more than 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the (13)C and (1)H assignment of 11 molecular solids with experimental shifts and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in more than 80% of cases. American Association for the Advancement of Science 2021-11-26 /pmc/articles/PMC8626066/ /pubmed/34826232 http://dx.doi.org/10.1126/sciadv.abk2341 Text en Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Physical and Materials Sciences Cordova, Manuel Balodis, Martins Simões de Almeida, Bruno Ceriotti, Michele Emsley, Lyndon Bayesian probabilistic assignment of chemical shifts in organic solids |
title | Bayesian probabilistic assignment of chemical shifts in organic solids |
title_full | Bayesian probabilistic assignment of chemical shifts in organic solids |
title_fullStr | Bayesian probabilistic assignment of chemical shifts in organic solids |
title_full_unstemmed | Bayesian probabilistic assignment of chemical shifts in organic solids |
title_short | Bayesian probabilistic assignment of chemical shifts in organic solids |
title_sort | bayesian probabilistic assignment of chemical shifts in organic solids |
topic | Physical and Materials Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626066/ https://www.ncbi.nlm.nih.gov/pubmed/34826232 http://dx.doi.org/10.1126/sciadv.abk2341 |
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