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.
Keywords: Malaria, multivariate image analysis, multiple linear regression, QSAR, PLS-DA, quinolon-4(1H)-imines.
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