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dc.contributor.author
Sarotti, Ariel Marcelo
dc.date.available
2016-06-06T21:24:51Z
dc.date.issued
2013-07
dc.identifier.citation
Sarotti, Ariel Marcelo; Successful combination of computationally inexpensive GIAO 13C NMR calculations and artificial neural network pattern recognition: a new strategy for simple and rapid detection of structural misassignments; Royal Society of Chemistry; Organic & Biomolecular Chemistry; 11; 29; 7-2013; 4847-4859
dc.identifier.issn
1477-0520
dc.identifier.uri
http://hdl.handle.net/11336/6080
dc.description.abstract
GIAO NMR chemical shift calculations coupled with trained artificial neural networks (ANNs) have been shown to provide a powerful strategy for simple, rapid and reliable identification of structural misassignments of organic compounds using only one set of both computational and experimental data. The geometry optimization, usually the most time-consuming step in the overall procedure, was carried out using computationally inexpensive methods (MM+, AM1 or HF/3-21G) and the NMR shielding constants at the affordable mPW1PW91/6-31G(d) level of theory. As low quality NMR prediction is typically obtained with such protocols, the decision making was foreseen as a problem of pattern recognition. Thus, given a set of statistical parameters computed after correlation between experimental and calculated chemical shifts the classification was done using the knowledge derived from trained ANNs. The training process was carried out with a set of 200 molecules chosen to provide a wide array of chemical functionalities and molecular complexity, and the results were validated with a set of 26 natural products that had been incorrectly assigned along with their 26 revised structures. The high prediction effectiveness observed makes this method a suitable test for rapid identification of structural misassignments, preventing not only the publication of wrong structures but also avoiding the consequences of such a mistake.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Royal Society of Chemistry
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Giao Nmr 13c Calculations
dc.subject
Artificial Neural Networks
dc.subject
Pattern Recognition
dc.subject
Structuralmissasignments
dc.subject.classification
Química Orgánica
dc.subject.classification
Ciencias Químicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Successful combination of computationally inexpensive GIAO 13C NMR calculations and artificial neural network pattern recognition: a new strategy for simple and rapid detection of structural misassignments
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-06-01T13:33:14Z
dc.journal.volume
11
dc.journal.number
29
dc.journal.pagination
4847-4859
dc.journal.pais
Reino Unido
dc.journal.ciudad
Cambridge
dc.description.fil
Fil: Sarotti, Ariel Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Química Rosario; Argentina
dc.journal.title
Organic & Biomolecular Chemistry
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://pubs.rsc.org/en/content/articlelanding/2013/ob/c3ob40843d
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1039/C3OB40843D
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