Quantitative Structure-activity Relationship (QSAR) Study of Toxicity of Substituted Aromatic Compounds to Photobacterium Phosphoreum

With the artificial neural network (ANN) method combined with the multiple linear regression (MLR), based on a series of quantum chemical descriptors and molecular connectivity indexes, quantitative structure-activity relationship (QSAR) models to predict the acute toxicity (-1gEC(50)) of substituted aromatic compounds to Photobacterium phosphoreum were established. Four molecular descriptors that appear in the MLR model, namely, the second order valence molecular connectivity index (X-2(V)), the energy of the highest occupied molecular orbital (E-HOMO), the logarithm of n-octyl alcohol/water partition coefficient (logK(ow)) and the Connolly molecular area (MA), were inputs of the ANN model. The root-mean-square error (RMSE) of the training and validation sets of the ANN model are 0.1359 and 0.2523, and the correlation coefficient (R) is 0.9810 and 0.8681, respectively. The leave-one-out (LOO) cross validated correlation coefficient (Q(LOO)(2)) of the MLR and ANN models is 0.6954 and 0.6708, respectively. The result showed that the two methods are complementary in the calculations. The regression method gave support to the neural network with physical explanation, and the neural network method gave a more accurate model for QSAR. In addition, some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.