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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/233344
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:30:32Zoai: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:34982025-09-29 10:30:32.574CONICET 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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
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Frontiers Media |
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Frontiers Media |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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