Machine learning in computational NMR-aided structural elucidation

Structure elucidation is a stage of paramount importance in the discovery of novel compounds because molecular structure determines their physical, chemical and biological properties. Computational prediction of spectroscopic data, mainly NMR, has become a widely used tool to help in such tasks due...

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Detalhes bibliográficos
Autores: Cortés, Iván, Cuadrado, Cristina, Hernández Daranas, Antonio, Sarotti, Ariel M.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/344200
Acesso em linha:http://hdl.handle.net/10261/344200
Access Level:acceso abierto
Palavra-chave:NMR
GIAO
machine learning
structural elucidation
artificial intelligence
NMR spectroscopy
Intelligence
Machine tools
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spelling Machine learning in computational NMR-aided structural elucidationCortés, IvánCuadrado, CristinaHernández Daranas, AntonioSarotti, Ariel M.NMRGIAOmachine learningstructural elucidationartificial intelligenceNMR spectroscopyartificial intelligenceNMR spectroscopyIntelligenceMachine toolsStructure elucidation is a stage of paramount importance in the discovery of novel compounds because molecular structure determines their physical, chemical and biological properties. Computational prediction of spectroscopic data, mainly NMR, has become a widely used tool to help in such tasks due to its increasing easiness and reliability. However, despite the continuous increment in CPU calculation power, classical quantum mechanics simulations still require a lot of effort. Accordingly, simulations of large or conformationally complex molecules are impractical. In this context, a growing number of research groups have explored the capabilities of machine learning (ML) algorithms in computational NMR prediction. In parallel, important advances have been made in the development of machine learning-inspired methods to correlate the experimental and calculated NMR data to facilitate the structural elucidation process. Here, we have selected some essential papers to review this research area and propose conclusions and future perspectives for the field.Our research was funded by the UNR (BIO 500 and 567), ANPCyT (PICT-2016-0116, PICT-2017-1524, and PICT-2019-4052), CONICET (PIP 11220200102205CO), MICINN (PID 2019-109476RB-C21), and ACIISI-FEDER (ProID2021010118).Peer reviewedFrontiers MediaUniversidad Nacional de Rosario (Argentina)Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (Argentina)Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina)Ministerio de Ciencia e Innovación (España)Agencia Canaria de Investigación, Innovación y Sociedad de la InformaciónEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/344200reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109476RB-C21https://doi.org/10.3389/fntpr.2023.1122426Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3442002026-05-22T06:33:51Z
dc.title.none.fl_str_mv Machine learning in computational NMR-aided structural elucidation
title Machine learning in computational NMR-aided structural elucidation
spellingShingle Machine learning in computational NMR-aided structural elucidation
Cortés, Iván
NMR
GIAO
machine learning
structural elucidation
artificial intelligence
NMR spectroscopy
artificial intelligence
NMR spectroscopy
Intelligence
Machine tools
title_short Machine learning in computational NMR-aided structural elucidation
title_full Machine learning in computational NMR-aided structural elucidation
title_fullStr Machine learning in computational NMR-aided structural elucidation
title_full_unstemmed Machine learning in computational NMR-aided structural elucidation
title_sort Machine learning in computational NMR-aided structural elucidation
dc.creator.none.fl_str_mv Cortés, Iván
Cuadrado, Cristina
Hernández Daranas, Antonio
Sarotti, Ariel M.
author Cortés, Iván
author_facet Cortés, Iván
Cuadrado, Cristina
Hernández Daranas, Antonio
Sarotti, Ariel M.
author_role author
author2 Cuadrado, Cristina
Hernández Daranas, Antonio
Sarotti, Ariel M.
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Nacional de Rosario (Argentina)
Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (Argentina)
Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina)
Ministerio de Ciencia e Innovación (España)
Agencia Canaria de Investigación, Innovación y Sociedad de la Información
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv NMR
GIAO
machine learning
structural elucidation
artificial intelligence
NMR spectroscopy
artificial intelligence
NMR spectroscopy
Intelligence
Machine tools
topic NMR
GIAO
machine learning
structural elucidation
artificial intelligence
NMR spectroscopy
artificial intelligence
NMR spectroscopy
Intelligence
Machine tools
description Structure elucidation is a stage of paramount importance in the discovery of novel compounds because molecular structure determines their physical, chemical and biological properties. Computational prediction of spectroscopic data, mainly NMR, has become a widely used tool to help in such tasks due to its increasing easiness and reliability. However, despite the continuous increment in CPU calculation power, classical quantum mechanics simulations still require a lot of effort. Accordingly, simulations of large or conformationally complex molecules are impractical. In this context, a growing number of research groups have explored the capabilities of machine learning (ML) algorithms in computational NMR prediction. In parallel, important advances have been made in the development of machine learning-inspired methods to correlate the experimental and calculated NMR data to facilitate the structural elucidation process. Here, we have selected some essential papers to review this research area and propose conclusions and future perspectives for the field.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/344200
url http://hdl.handle.net/10261/344200
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109476RB-C21
https://doi.org/10.3389/fntpr.2023.1122426
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
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