Quantitative Structure-activity Relationships of Imidazole-containing Farnesyltransferase Inhibitors Using Different Chemometric Methods

Ali      Shayanfar; Saeed      Ghasemi; Somaieh      Soltani; Karim      Asadpour-Zeynali; Robert      J. Doerksen; Abolghasem      Jouyban

doi:10.2174/1573406411309030014

Abstract

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