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metadata.artigo.dc.title: Exploring MIA-QSAR´s for antimalarial quinolon-4(1H)-imines
metadata.artigo.dc.creator: Duarte, Mariene H.
Barigye, Stephen J.
Freitas, Matheus P.
metadata.artigo.dc.subject: Malaria
Multivariate image analysis
multiple linear regression
Multivariate image analysis (MIA)
Partial least squares-discriminant analysis (PLS-DA)
metadata.artigo.dc.publisher: Bentham Science Publishers 2015
metadata.artigo.dc.identifier.citation: DUARTE, M. H.; BARIGYE, S. J.; FREITAS, M. P. Exploring MIA-QSAR´s for antimalarial quinolon-4(1H)-imines. Combinatorial Chemistry & High Throughput Screening, [S.l.], v. 18, n. 2, p. 208-216, 2015. DOI: 10.2174/1386207318666141229123349.
metadata.artigo.dc.description.abstract: A series of quinolon-4(1H)-imines have been recently discovered as antimalarials, targeting both the exoerythrocytic and erythrocytic stages of the parasite’s development stages, which correspond to the phase of clinical symptoms. Endowed with chemical and metabolic stability, the quinolon-4(1H)- imines are thus presented as promissory dual-stage antimalarials. Three versions of multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) methods, namely traditional MIA-QSAR, augmented MIA-QSAR (aug-MIA-QSAR) and color-encoded aug-MIA-QSAR (aug- MIA-QSARcolor), were applied to model the antimalarial activities in this series of compounds. The multiple linear regression models indicated that the aug-MIA-QSAR method is more predictive and reliable than the others (R2 = 0.8079, R2cv = 0.6647 and R2pred = 0.9691) for this series of compounds. The selected aug- MIA-QSAR descriptors were used for pattern recognition using discriminant analysis by partial least squares (PLS-DA), in order to separate compounds with low, moderate and high bioactivities.
metadata.artigo.dc.language: en_US
Appears in Collections:DQI - Artigos publicados em periódicos

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