QSAR Study on Substituted Aromatic Compounds Using Artificial Neural Network

The systematic study of the quantitative structure-activity relationship(QSAR) on 24 substituted aromatic compounds was performed by the artificial neural network based on the back propagation algorithm.For the artificial neural network method,when using the quantum chemical parameters about structure as the inputs of the neural network and the acute toxicities as the outputs of the neural network,the correlation coefficient was 0.9834,the leave one out cross-validation regression coefficient was 0.9780,the standard error was 0.11,the correlation coefficient of the test set was 0.9955 and the absolute values of residual were less than 0.33.In order to make contrast,the QSAR model was set up by multiple linear regressions(MLR) method.For the model built by MLR,the correlation coefficient was 0.9786,the standard error was 0.12,the absolute values of residual were less than 0.36 and the correlation coefficient of the test set was 0.9904.The results showed that the performance of neural network method was better than that of MLR method.