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dc.contributor.authorLuque Ruiz, Irene
dc.contributor.authorGómez-Nieto, Miguel Ángel
dc.date.accessioned2018-11-02T09:11:35Z
dc.date.available2018-11-02T09:11:35Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10396/17393
dc.description.abstractThe reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithmses_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceMolecules 23, 2756 (2018)es_ES
dc.subjectQSARes_ES
dc.subjectClassification modeles_ES
dc.subjectApplicability domaines_ES
dc.subjectRivality indexes_ES
dc.subjectModelability indexes_ES
dc.titleStudy of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexeses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/molecules23112756es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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