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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 Marta Sarotti, Ariel M. |
Palabras clave : | ESTRUCTURA QUIMICA; ESTRUCTURA MOLECULAR; QUIMICA TEORICA Y COMPUTACIONAL; CALCULOS 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 |
Appears in Collections: | Artículos |
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giao-c-h-cosy-simulations-merged.pdf | 2,86 MB | Adobe PDF | View/Open |
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