Toxicity Assessment of the Binary Mixtures of Aquatic Organisms Based on Different Hypothetical Descriptors

Modern industrialization has led to the creation of a wide range of organic chemicals, especially in the form of multicomponent mixtures, thus making the evaluation of environmental pollution more difficult by normal methods. In this paper, we attempt to use forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNN) to establish quantitative structure-activity relationship models (QSARs) to predict the toxicity of 79 binary mixtures of aquatic organisms using different hypothetical descriptors. To search for the proper mixture descriptors, 11 mixture rules were performed and tested based on preliminary modeling results. The statistical parameters of the best derived MLR model were N_train = 62, R² = 0.727, RMS = 0.494, F = 159.537, Q²_LOO = 0.727, and Q²_pred = 0.725 for the training set; and N_test = 17, R² = 0.721, RMS = 0.508, F = 38.773, and q²_ext = 0.720 for the external test set. The RBFNN model gave the following statistical results: N_train = 62, R² = 0.956, RMS = 0.199, F = 1279.919, Q²_LOO = 0.955, and Q²_pred = 0.855 for the training set; and N_test = 17, R² = 0.880, RMS = 0.367, F = 110.980, and q²_ext = 0.853 for the external test set. The quality of the models was assessed by validating the relevant parameters, and the final results showed that the developed models are predictive and can be used for the toxicity prediction of binary mixtures within their applicability domain.