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dc.contributor.authorGuerra, Ángela-
dc.contributor.authorGonzález-Naranjo, Pedro-
dc.contributor.authorCampillo, Nuria E.-
dc.contributor.authorCerecetto, Hugo-
dc.contributor.authorGonzález, Mercedes-
dc.contributor.authorPáez, Juan A.-
dc.date.accessioned2014-04-10T10:53:04Z-
dc.date.available2014-04-10T10:53:04Z-
dc.date.issued2013-
dc.identifier.citationCurrent Computer-Aided Drug Design 9: 130- 140 (2013)-
dc.identifier.issn1573-4099-
dc.identifier.urihttp://hdl.handle.net/10261/95347-
dc.description.abstractA supervised artificial neural network model has been developed for the accurate prediction of the anti-T. cruzi activity of heterogeneous series of compounds. A representative set of 72 compounds of wide structural diversity was chosen in this study. The definition of the molecules was achieved from an unsupervised neural network using a new methodology, CODES program. This program codifies each molecule into a set of numerical parameters taking into account exclusively its chemical structure. The final model shows high average accuracy of 84% (training performance) and predictability of 77% (external validation performance) for the 4:4:1 architecture net with different training set and external prediction test. This approach using CODES methodology represents a useful tool for the prediction of pharmacological properties. CODES© is available free of charge for academic institutions. © 2013 Bentham Science Publishers.-
dc.publisherBentham Science Publishers-
dc.rightsclosedAccess-
dc.subjecttrypanocidal-
dc.subjectCODES-
dc.subjectChagas disease-
dc.subjectQSAR-
dc.subjectTrypanosoma cruzi-
dc.subjectcompounds-
dc.subjectin silico-
dc.subjectmolecules-
dc.subjectneural network-
dc.subjectPharmacology-
dc.titleArtificial neural networks based on CODES descriptors in pharmacology: Identification of novel trypanocidal drugs against chagas disease-
dc.typeartículo-
dc.identifier.doihttp://dx.doi.org/10.2174/1573409911309010012-
dc.identifier.e-issn1875-6697-
dc.date.updated2014-04-10T10:53:05Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
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