Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/41803
metadata.artigo.dc.title: MIA-QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives
metadata.artigo.dc.creator: Goodarzi, Mohammad
Freitas, Matheus P.
metadata.artigo.dc.subject: TIBO derivatives
Anti-HIV reverse transcriptase activities
MIA-QSAR
PCA-ANFIS
Multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR)
Principal component analysis (PCA)
Adaptive neuro-fuzzy inference system (ANFIS)
metadata.artigo.dc.publisher: Elsevier
metadata.artigo.dc.date.issued: Apr-2010
metadata.artigo.dc.identifier.citation: GOODARZI, M.; FREITAS, M. P. MIA-QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives. European Journal of Medicinal Chemistry, [S.l.], v. 45, n. 4, p. 1352-1358, Apr. 2010. DOI: 10.1016/j.ejmech.2009.12.028.
metadata.artigo.dc.description.abstract: The activities of a series of HIV reverse transcriptase inhibitor TIBO derivatives were recently modeled by using genetic function approximation (GFA) and artificial neural networks (ANN) on topological, structural, electronic, spatial and physicochemical descriptors. The prediction results were found to be superior to those previously established. In the present work, the multivariate image analysis applied to quantitative structure–activity relationship (MIA–QSAR) method coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS), which accounts for non-linearities, was applied on the same set of compounds previously reported. Additionally, partial least squares (PLS) and multilinear partial least squares (N-PLS) regressions were used for comparison with the MIA–QSAR/PCA–ANFIS model. The ANFIS procedure was capable of accurately correlating the inputs (PCA scores) with the bioactivities. The predictive performance of the MIA–QSAR/PCA–ANFIS model was significantly better than the MIA–QSAR/PLS and N-PLS models, as well as than the reported models based on CoMFA, CoMSIA, OCWLGI and classical descriptors, suggesting that the present methodology may be useful to solve other QSAR problems, specially those involving non-linearities.
metadata.artigo.dc.identifier.uri: https://www.sciencedirect.com/science/article/abs/pii/S0223523409006722
http://repositorio.ufla.br/jspui/handle/1/41803
metadata.artigo.dc.language: en_US
Appears in Collections:DQI - Artigos publicados em periódicos

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