Prediction of the baseline toxicity of non-polar narcotic chemical mixtures by QSAR approach.

Environmental contaminants are frequently encountered as mixtures, and research on mixture toxicity is a hot topic until now. In the present study, the mixture toxicity of non-polar narcotic chemical was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNNs) from molecular descriptors that are calculated and be defined as composite descriptors according to the fractional concentrations of the mixture components. The statistical parameters provided by the MLR model were R(2)=0.9512, RMS=0.3792, F=1402.214 and LOOq(2)=0.9462 for the training set, and R(2)=0.9453, RMS=0.3458, F=276.671 and q(ext)(2)=0.9450 for the external test set. The RBFNN model gave the following statistical results, namely: R(2)=0.9779, RMS=0.2498, F=3188.202 and LOOq(2)=0.9746 for the training set, and R(2)=0.9763, RMS=0.2358, F=660.631 and q(ext)(2)=0.9745, for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction mixture toxicity of non-polar narcotic chemicals. The RBFNN model gave even improved results. In addition, ε(LUMO+1) (the energy of the second lowest unoccupied molecular orbital) and PPSA (total charge weighted partial positively surface area) were found to have high correlation with the mixture toxicity.