Multivariate adaptive regression splines (MARSplines) have been applied for the quantitative structure-activity
relationships (QSAR) studies of antitumor activity of acridinone derivatives. Molecular modeling studies were performed
with the use of HyperChem and Dragon software. The structures of the compounds were firstly pre-optimized with the
MM+ mechanics and semi-empirical AM1 method procedure included in the HyperChem and resulting geometrical
structures were studied with the use of Dragon software, and several molecular descriptors of acridinones were calculated
and used as predictor (independent) variables in the MARS model building. Principal component analysis (PCA) was used
to select the training and test sets. The optimal MARS model uses 28 basis functions to describe acridinones' antitumor
activity and characterized high correlation between predicted antitumor activity and that one from biological experiments
for the data used in the training and testing sets of acridinones with correlation coefficients on the level of 0.9477 and
0.9660, respectively. Generally, results showed that MARS model provided powerful capacity of prediction of antitumor
activity of acridinone derivatives. Moreover, a physicochemical explanation of the descriptors selected by MARSplines
analysis is also given, and indicated that molecular parameters describing 3-D properties as well as lipophilicity of acridinone
derivative molecule are important for acridinones antitumor activity.
Keywords: Acridinones, Antitumor activity, Multivariate Adaptive Regression Splines (MARSplines), Molecular descriptors,
Prediction of activity, Quantitative structure-activity relationships (QSAR).
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