Farnesyltranseferase inhibitors (FTIs) are one of the most promising classes of anticancer agents, but though
some compounds in this category are in clinical trials there are no marketed drugs in this class yet. Quantitative structureactivity
relationship (QSAR) models can be used for predicting the activity of FTI candidates in early stages of drug discovery.
In this study 192 imidazole-containing FTIs were obtained from the literature, structures of the molecules were
optimized using Hyperchem software, and molecular descriptors were calculated using Dragon software. The most suitable
descriptors were selected using genetic algorithms-partial least squares (GA-PLS) and stepwise regression, and indicated
that the volume, shape and polarity of the FTIs are important for their activities. 2D-QSAR models were prepared
using both linear methods, i.e., multiple linear regression (MLR), and non-linear methods, i.e., artificial neural networks
(ANN) and support vector machines (SVM). The proposed QSAR models were validated using internal and external validation
methods. The results show that the proposed 2D-QSAR models are valid and that they can be applied to predict the
activities of imidazole-containing FTIs. The prediction capability of the 2D-QSAR (linear and non-linear) models is comparable
to and somewhat better than that of previous 3D-QSAR models and the non-linear models are more accurate than
the linear models.
Keywords: Imidazole-containing farnesyltransferase inhibitors, cancer, QSAR, multiple linear regression, artificial neural network, support vector machine, Plasmodium falciparum, 3D-QSAR, COMFA, COMSIA
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