QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

Abstract

Quantitative structure activity relationship (QSAR) for 21 insecticides of phthalamides containing hydrazone (PCH) was studied using multiple linear regression (MLR), principle component regression (PCR) and artificial neural network (ANN). Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA) were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84). Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl)-5-chloro-N’-((5-methylthiophen-2-yl)methylene) benzohydrazide, 2-(decalinecarbamoyl)-5-chloro-N’-((thiophen-2-yl)-methylene) benzohydrazide and 2-(decaline carbamoyl)-N’-(4-fluorobenzylidene)-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

Multivariate statistical analysis methods in QSAR

“…There are reports of application of PCR in different elds of QSAR. 83,84 Xiao Li et al correlated the toxicity of PAHs with physical and chemical properties QSAR descriptors by PCR method. 85 Compared to multiple regression analysis, the advantage of PCR is that existing collinearities between variables is not a disturbing factor, and that the number of variables presents in the analysis, can be more than the number of observations.…”