Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure–activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R(2) (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq(2) (leave-one-out correlation coefficient) = 0.864, F (Fisher’s test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and N(ext) (number of compounds in external test set) = 20, R(2) = 0.853, [Formula: see text] (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R(2) = 0.925, LOOq(2) = 0.924, F = 950.686, RMS = 0.447 for the training set, and N(ext) = 20, R(2) = 0.896, [Formula: see text] = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.