Please use this identifier to cite or link to this item: https://repositorio.uca.edu.ar/handle/123456789/11469
Título : GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward
Autor : Zanardi, María M. 
Sarotti, Ariel M. 
Palabras clave : ESTRUCTURA QUIMICAESTRUCTURA MOLECULARQUIMICA TEORICA Y COMPUTACIONALCALCULOS QUIMICOS
Fecha de publicación : 2015
Editorial : ACS Publications
Cita : Zanardi, M.M., Sarotti, A.M. GIAO C−H COSY simulations merged with artificial neural networks pattern recognition analysis: pushing the structural validation a step forward [en línea]. The Journal of Organic Chemistry. 2015 (80). Disponible en: https://repositorio.uca.edu.ar/handle/123456789/11469
Resumen : 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.
URI : https://repositorio.uca.edu.ar/handle/123456789/11469
ISSN : 1520-6904
1520-6904 (Online)
Disciplina: QUIMICA
DOI: 10.1021/acs.joc.5b01663
Derechos: Acceso abierto
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