Critical comparative analysis, validation and interpretation of SVM and PLS regression models in a QSAR study on HIV-1 protease inhibitors

dc.creatorHernandez, N
dc.creatorKiralj, R
dc.creatorFerreira, MMC
dc.creatorTalavera, I
dc.date2009
dc.dateAUG 15
dc.date2014-11-15T23:54:29Z
dc.date2015-11-26T17:36:01Z
dc.date2014-11-15T23:54:29Z
dc.date2015-11-26T17:36:01Z
dc.date.accessioned2018-03-29T00:18:31Z
dc.date.available2018-03-29T00:18:31Z
dc.identifierChemometrics And Intelligent Laboratory Systems. Elsevier Science Bv, v. 98, n. 1, n. 65, n. 77, 2009.
dc.identifier0169-7439
dc.identifierWOS:000268657300009
dc.identifier10.1016/j.chemolab.2009.04.012
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/57372
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/57372
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/57372
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1285811
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFour Quantitative Structure-Activity Relationship (QSAR) models were constructed for a set of 32 and 16 HIV-1 protease inhibitors in the training and external validation sets, respectively, using the biological activity and molecular descriptors from the literature. Two QSAR models were based on Support Vector Machines methods (SVM): Support Vector Regression (SVR) and Least-Squares Support Vector Machines (LS-SVM) models. The other two models were an ordinary Partial Least Squares (PLS) and Ordered Predictors Selection-based PLS (OPS-PLS). The SVR and LS-SVM models showed to be somewhat better than the PLS model in external validation and leave-N-out crossvalidation. SVR and LS-SVM were better than OPS-PLS in external validation, but showed equal performance in leave-N-out crossvalidation. However, despite of their high predictive ability, the SVM models failed in y-randomization, which did not happen with the PLS and OPS-PLS models. The OPS-PLS model was the only one that undoubtedly showed satisfactory performance both in prediction and all validations. The selection of inhibitors by the SVM-based models and variable selection by the OPS-PLS model were rationalized by means of Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). Lagrange multipliers from the SVR and LS-SVM models were explained for the first time in terms of molecular structures, descriptors, biological activity and principal components. Some unresolved difficulties in practical usage of SVM in QSAR and QSPR were pointed out. The presented validation and interpretation of SVR and LS-SVM models is a proposal for future investigations about SVM applications in QSAR and QSPR, valid for any modeling and validation condition of the final regression equations. (C) 2009 Elsevier B.V. All rights reserved.
dc.description98
dc.description1
dc.description65
dc.description77
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationChemometrics And Intelligent Laboratory Systems
dc.relationChemometrics Intell. Lab. Syst.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectPeptidic protease inhibitors
dc.subjectMolecular descriptors
dc.subjectRegression models
dc.subjectValidation
dc.subjectStatistics
dc.subjectSupport Vector Regression
dc.subjectMolecular Descriptors
dc.subjectA-priori
dc.subjectPrediction
dc.subjectMachines
dc.subjectSelection
dc.subjectGraphics
dc.subjectTutorial
dc.subjectQspr
dc.titleCritical comparative analysis, validation and interpretation of SVM and PLS regression models in a QSAR study on HIV-1 protease inhibitors
dc.typeArtículos de revistas


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