Application of Wavelet and Genetic Algorithms for QSAR Study on 5-Lipoxygenase Inhibitors and Design New Compounds

Abstract

A quantitative structure-activity relationship (QSAR) modeling was carried out for the prediction of inhibitory activity of 1-phenyl[2H]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. Partial least squares (PLS) algorithm was employed to model the relationships between molecular descriptors and inhibitory activity of molecules using the genetic algorithm (GA) method as variable selection tool. Pre-processing methods such as wavelet transform (WT) were also used to enhance the predictive power of multivariate calibration methods. To evaluate the models applied in this study (PLS, GA-PLS and WT-GA-PLS), the inhibitory activities of several compounds, not included in the modeling procedure, were predicted. The results of models showed high prediction ability with root mean square error of prediction 0.194, 0.161 and 0.140 for PLS, GA-PLS and WT-GA-PLS, respectively. The WT-GA-PLS method was employed to predict the inhibitory activity of the new inhibitor derivatives.