Prediction of LD50 for aniline derivatives (Including some herbicide compounds) using computational methods

In recent decades, computational methods with regard to accurate validation parameters for the determination of the physical- chemistry properties of compounds have been considered by many researchers and have been used as an economic and environmental alternative to saving time and eliminating high costs.  In this study, the relationship between the logarithmic values of LD50</sub>,) log (LD50</sub>)(molkg-1</sup>) and molecular descriptors has been investigated for 60 types of aniline derivatives(including some herbicides compounds). At first, the structure of the compounds were drawn by Gauss view05 software and optimized using Gaussian 09 software with B3LYP/6-311++G** method, and then were extracted molecular descriptors. Then inappropriate descriptors were eliminated by genetic algorithm method and the best ones were used for multiple linear regression (MLR) and artificial neural networks (ANN) models. The results showed that the ANN method with the lowest error and the highest coefficient of determination was higher than the MLR method to predicting the log(LD50</sub>)(molkg-1</sup>) of studied aniline derivatives.