NMR shift prediction from small data quantities

Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model that is able t...

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Autores principales: Rull, Herman, Fischer, Markus, Kuhn, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683292/
https://www.ncbi.nlm.nih.gov/pubmed/38012793
http://dx.doi.org/10.1186/s13321-023-00785-x
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author Rull, Herman
Fischer, Markus
Kuhn, Stefan
author_facet Rull, Herman
Fischer, Markus
Kuhn, Stefan
author_sort Rull, Herman
collection PubMed
description Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model that is able to achieve better results than other models for relevant datasets with comparatively low amounts of data. We show this by predicting [Formula: see text] and [Formula: see text] NMR chemical shifts of small molecules in specific solvents. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00785-x.
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spelling pubmed-106832922023-11-30 NMR shift prediction from small data quantities Rull, Herman Fischer, Markus Kuhn, Stefan J Cheminform Research Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model that is able to achieve better results than other models for relevant datasets with comparatively low amounts of data. We show this by predicting [Formula: see text] and [Formula: see text] NMR chemical shifts of small molecules in specific solvents. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00785-x. Springer International Publishing 2023-11-27 /pmc/articles/PMC10683292/ /pubmed/38012793 http://dx.doi.org/10.1186/s13321-023-00785-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Rull, Herman
Fischer, Markus
Kuhn, Stefan
NMR shift prediction from small data quantities
title NMR shift prediction from small data quantities
title_full NMR shift prediction from small data quantities
title_fullStr NMR shift prediction from small data quantities
title_full_unstemmed NMR shift prediction from small data quantities
title_short NMR shift prediction from small data quantities
title_sort nmr shift prediction from small data quantities
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683292/
https://www.ncbi.nlm.nih.gov/pubmed/38012793
http://dx.doi.org/10.1186/s13321-023-00785-x
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