Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/41417
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dc.creatorGoodarzi, Mohammad-
dc.creatorFreitas, Matheus P.-
dc.date.accessioned2020-06-14T22:57:38Z-
dc.date.available2020-06-14T22:57:38Z-
dc.date.issued2009-03-
dc.identifier.citationGOODARZI, M.; FREITAS, M. P. On the use of PLS and N-PLS in MIA-QSAR: Azole antifungals. Chemometrics and Intelligent Laboratory Systems, [S.l.], v. 96, n. 1, p. 59-62, Mar. 2009. DOI: 10.1016/j.chemolab.2008.11.007.pt_BR
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S0169743908002104pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/41417-
dc.description.abstractThe antifungal activities of a series of azole derivatives have been modeled by using MIA (multivariate image analysis) descriptors. Two regression methods were applied to correlate such descriptors with the activities column vector: bilinear (classical) and multilinear (N-way) partial least squares - PLS and N-PLS, respectively. The PLS-based model for this series of compounds demonstrated higher predictive ability than the N-PLS-based model, in opposition to some published results for other series of compounds. The activities block was taken in logarithmic scale (pMIC90(cpd)/pMIC90(bifonazole)) and the statistical performance of both models was found to be significantly better than the CoMFA analysis previously established.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceChemometrics and Intelligent Laboratory Systemspt_BR
dc.subjectMIA-QSARpt_BR
dc.subjectPLS regressionpt_BR
dc.subjectN-PLS regressionpt_BR
dc.subjectAntifungalspt_BR
dc.subjectMultivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR)pt_BR
dc.subjectPartial least squares regressionpt_BR
dc.subjectMultiway partial least squares regressionpt_BR
dc.titleOn the use of PLS and N-PLS in MIA-QSAR: Azole antifungalspt_BR
dc.typeArtigopt_BR
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