In the present study, predictive quantitative structure – activity relationship (QSAR) models for anti-malarial
activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet
(http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of
anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem
of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of
prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single
descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r2> 0.85 and r2> 0.78 for subtraining
and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q2 = 0.83,
R2 = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen
at a topological distance of six.
Keywords: Anti-malarial activity, 4-aminoquinolines, QSAR, optimal descriptor, CORAL software.
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