GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward

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Abstract: The structural validation problem using quantum chemistry approaches (confirm or reject a candidate structure) has been tackled with artificial neural network (ANN) mediated multidimensional pattern recognition from experimental and calculated 2D C−H COSY. In order to identify subtle...

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Detalles Bibliográficos
Autores principales: Zanardi, María M., Sarotti, Ariel M.
Formato: Artículo
Lenguaje:Inglés
Publicado: ACS Publications 2021
Materias:
ESTRUCTURA QUIMICA
ESTRUCTURA MOLECULAR
QUIMICA TEORICA Y COMPUTACIONAL
CALCULOS QUIMICOS
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/11469
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139123456789-11469
record_format dspace
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic ESTRUCTURA QUIMICA
ESTRUCTURA MOLECULAR
QUIMICA TEORICA Y COMPUTACIONAL
CALCULOS QUIMICOS
spellingShingle ESTRUCTURA QUIMICA
ESTRUCTURA MOLECULAR
QUIMICA TEORICA Y COMPUTACIONAL
CALCULOS QUIMICOS
Zanardi, María M.
Sarotti, Ariel M.
GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward
topic_facet ESTRUCTURA QUIMICA
ESTRUCTURA MOLECULAR
QUIMICA TEORICA Y COMPUTACIONAL
CALCULOS QUIMICOS
description Abstract: The structural validation problem using quantum chemistry approaches (confirm or reject a candidate structure) has been tackled with artificial neural network (ANN) mediated multidimensional pattern recognition from experimental and calculated 2D C−H COSY. In order to identify subtle errors (such as regio- or stereochemical), more than 400 ANNs have been built and trained, and the most efficient in terms of classification ability were successfully validated in challenging real examples of natural product misassignments.
format Artículo
author Zanardi, María M.
Sarotti, Ariel M.
author_facet Zanardi, María M.
Sarotti, Ariel M.
author_sort Zanardi, María M.
title GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward
title_short GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward
title_full GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward
title_fullStr GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward
title_full_unstemmed GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward
title_sort giao c−h cosy simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward
publisher ACS Publications
publishDate 2021
url https://repositorio.uca.edu.ar/handle/123456789/11469
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AT sarottiarielm giaochcosysimulationsmergedwithartificialneuralnetworkspatternrecognitionanalysispushingthestructuralvalidationastepforward
bdutipo_str Repositorios
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