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metadata.artigo.dc.title: On the use of PLS and N-PLS in MIA-QSAR: Azole antifungals
metadata.artigo.dc.creator: Goodarzi, Mohammad
Freitas, Matheus P.
metadata.artigo.dc.subject: MIA-QSAR
PLS regression
N-PLS regression
Multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR)
Partial least squares regression
Multiway partial least squares regression
metadata.artigo.dc.publisher: Elsevier Mar-2009
metadata.artigo.dc.identifier.citation: GOODARZI, 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.
metadata.artigo.dc.description.abstract: The 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.
metadata.artigo.dc.language: en_US
Appears in Collections:DQI - Artigos publicados em periódicos

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