Machine learning in computational NMR-aided structural elucidation

Autores
Cortés, Iván; Cuadrado, Cristina; Hernández Daranas, Antonio; Sarotti, Ariel Marcelo
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Structure elucidation is a stage of paramount importance in the discovery of novelcompounds because molecular structure determines their physical, chemical andbiological properties. Computational prediction of spectroscopic data, mainly NMR,has become a widely used tool to help in such tasks due to its increasing easiness andreliability. 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 thiscontext, a growing number of research groups have explored the capabilities ofmachine learning (ML) algorithms in computational NMR prediction. In parallel,important advances have been made in the development of machine learninginspiredmethods to correlate the experimental and calculated NMR data to facilitatethe structural elucidation process. Here, we have selected some essential papers toreview this research area and propose conclusions and future perspectives for thefield.
Fil: Cortés, Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Cuadrado, Cristina. Consejo Superior de Investigaciones Científicas; España
Fil: Hernández Daranas, Antonio. Consejo Superior de Investigaciones Científicas; España
Fil: Sarotti, Ariel Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Materia
NMR
GIAO
MACHINE LEARNING, STRUCTURAL ELUCIDATION
ARTIFICIAL INTELLIGENCE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/233344
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Machine learning in computational NMR-aided structural elucidationCortés, IvánCuadrado, CristinaHernández Daranas, AntonioSarotti, Ariel MarceloNMRGIAOMACHINE LEARNING, STRUCTURAL ELUCIDATIONARTIFICIAL INTELLIGENCEhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Structure elucidation is a stage of paramount importance in the discovery of novelcompounds because molecular structure determines their physical, chemical andbiological properties. Computational prediction of spectroscopic data, mainly NMR,has become a widely used tool to help in such tasks due to its increasing easiness andreliability. 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 thiscontext, a growing number of research groups have explored the capabilities ofmachine learning (ML) algorithms in computational NMR prediction. In parallel,important advances have been made in the development of machine learninginspiredmethods to correlate the experimental and calculated NMR data to facilitatethe structural elucidation process. Here, we have selected some essential papers toreview this research area and propose conclusions and future perspectives for thefield.Fil: Cortés, Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFil: Cuadrado, Cristina. Consejo Superior de Investigaciones Científicas; EspañaFil: Hernández Daranas, Antonio. Consejo Superior de Investigaciones Científicas; EspañaFil: Sarotti, Ariel Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFrontiers Media2023-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/233344Cortés, Iván; Cuadrado, Cristina; Hernández Daranas, Antonio; Sarotti, Ariel Marcelo; Machine learning in computational NMR-aided structural elucidation; Frontiers Media; Frontiers in Natural Products; 2; 1-2023; 1-112813-2602CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fntpr.2023.1122426/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fntpr.2023.1122426info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:03:00Zoai:ri.conicet.gov.ar:11336/233344instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 12:03:00.602CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
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 Marcelo
author Cortés, Iván
author_facet Cortés, Iván
Cuadrado, Cristina
Hernández Daranas, Antonio
Sarotti, Ariel Marcelo
author_role author
author2 Cuadrado, Cristina
Hernández Daranas, Antonio
Sarotti, Ariel Marcelo
author2_role author
author
author
dc.subject.none.fl_str_mv NMR
GIAO
MACHINE LEARNING, STRUCTURAL ELUCIDATION
ARTIFICIAL INTELLIGENCE
topic NMR
GIAO
MACHINE LEARNING, STRUCTURAL ELUCIDATION
ARTIFICIAL INTELLIGENCE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Structure elucidation is a stage of paramount importance in the discovery of novelcompounds because molecular structure determines their physical, chemical andbiological properties. Computational prediction of spectroscopic data, mainly NMR,has become a widely used tool to help in such tasks due to its increasing easiness andreliability. 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 thiscontext, a growing number of research groups have explored the capabilities ofmachine learning (ML) algorithms in computational NMR prediction. In parallel,important advances have been made in the development of machine learninginspiredmethods to correlate the experimental and calculated NMR data to facilitatethe structural elucidation process. Here, we have selected some essential papers toreview this research area and propose conclusions and future perspectives for thefield.
Fil: Cortés, Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Cuadrado, Cristina. Consejo Superior de Investigaciones Científicas; España
Fil: Hernández Daranas, Antonio. Consejo Superior de Investigaciones Científicas; España
Fil: Sarotti, Ariel Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
description Structure elucidation is a stage of paramount importance in the discovery of novelcompounds because molecular structure determines their physical, chemical andbiological properties. Computational prediction of spectroscopic data, mainly NMR,has become a widely used tool to help in such tasks due to its increasing easiness andreliability. 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 thiscontext, a growing number of research groups have explored the capabilities ofmachine learning (ML) algorithms in computational NMR prediction. In parallel,important advances have been made in the development of machine learninginspiredmethods to correlate the experimental and calculated NMR data to facilitatethe structural elucidation process. Here, we have selected some essential papers toreview this research area and propose conclusions and future perspectives for thefield.
publishDate 2023
dc.date.none.fl_str_mv 2023-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/233344
Cortés, Iván; Cuadrado, Cristina; Hernández Daranas, Antonio; Sarotti, Ariel Marcelo; Machine learning in computational NMR-aided structural elucidation; Frontiers Media; Frontiers in Natural Products; 2; 1-2023; 1-11
2813-2602
CONICET Digital
CONICET
url http://hdl.handle.net/11336/233344
identifier_str_mv Cortés, Iván; Cuadrado, Cristina; Hernández Daranas, Antonio; Sarotti, Ariel Marcelo; Machine learning in computational NMR-aided structural elucidation; Frontiers Media; Frontiers in Natural Products; 2; 1-2023; 1-11
2813-2602
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fntpr.2023.1122426/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fntpr.2023.1122426
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.928904

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