Automatic identification of terpenoid skeletons by feed-forward neural networks

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniv Reims
dc.date.accessioned2014-05-20T15:21:06Z
dc.date.accessioned2022-10-05T16:09:04Z
dc.date.available2014-05-20T15:21:06Z
dc.date.available2022-10-05T16:09:04Z
dc.date.created2014-05-20T15:21:06Z
dc.date.issued2006-10-10
dc.identifierAnalytica Chimica Acta. Amsterdam: Elsevier B.V., v. 579, n. 2, p. 217-226, 2006.
dc.identifier0003-2670
dc.identifierhttp://hdl.handle.net/11449/32282
dc.identifier10.1016/j.aca.2006.07.023
dc.identifierWOS:000241473700010
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3904868
dc.description.abstractFeed-forward neural networks (FFNNs) were used to predict the skeletal type of molecules belonging to six classes of terpenoids. A database that contains the (13)C NMR spectra of about 5000 compounds was used to train the FFNNs. An efficient representation of the spectra was designed and the constitution of the best FFNN input vector format resorted from an heuristic approach. The latter was derived from general considerations on terpenoid structures. (c) 2006 Elsevier B.V. All rights reserved.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationAnalytica Chimica Acta
dc.relation5.123
dc.relation1,512
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectartificial neural networks
dc.subject(13)C NMR
dc.subjectspectroscopy
dc.subjectterpenoids
dc.subjectsteroids
dc.titleAutomatic identification of terpenoid skeletons by feed-forward neural networks
dc.typeArtigo


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