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

https://doi.org/10.22146/ijc.21273

Adi Syahputra(1*), Mudasir Mudasir(2), Nuryono Nuryono(3), Anifuddin Aziz(4), Iqmal Tahir(5)

(1) Austrian-Indonesian Centre (AIC) for Computational Chemistry, Universitas Gadjah Mada, Sekip Utara Yogyakarta 55281
(2) Austrian-Indonesian Centre (AIC) for Computational Chemistry, Universitas Gadjah Mada, Sekip Utara Yogyakarta 55281; Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281
(3) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281
(4) Computer Sciences Study Program, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281
(5) Austrian-Indonesian Centre (AIC) for Computational Chemistry, Universitas Gadjah Mada, Sekip Utara Yogyakarta 55281; Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281
(*) Corresponding Author

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.

Keywords


QSAR; phathalamide; hydrazone; multiple linear regression; principle component regression; artificial neural network

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DOI: https://doi.org/10.22146/ijc.21273

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