QSAR of 3-isothiazolinone compounds using artificial neural network

The study of the quantitative structure-activity relationship(QSAR) on 21 kinds of 2-(4-substi-tuted-phenyl)-3-isothiazolinones was established by the artificial neural network based on the back propagation algorithm.For the artificial neural network method,the quantum chemical parameters about structure and the mo-lecular topological index were used as the inputs of the neural network,and the antibacterial activities as the out-puts of the neural network.As a result,the leave-one-out cross-validation regression coefficient was 0.9916;the standard error was 0.0801;the correlation coefficient of the test set was 0.9731 and the absolute values of resid-ual were less than 0.221.For comparison,the QSAR model was set up by multiple linear regressions(MLR) method.For the model built by MLR,the correlation coefficient was 0.8418;the standard error was 0.3039 and the absolute values of residual were less than 0.636.The results showed that the performance of neural network method is better than that of MLR method.