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

机译：QSAR方法预测非极性麻醉药混合物的基线毒性

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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 LOO_q~2 = 0.9462 for the training set, and R~2 = 0.9453, RMS = 0.3458, F = 276.671 and q_(ext)~ = 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 LOO_q~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.
机译：环境污染物通常以混合物形式遇到，到目前为止，关于混合物毒性的研究一直是热门话题。在本研究中，通过线性和非线性统计方法对非极性麻醉药品的混合毒性进行建模，也就是说，通过分子描述子的正向逐步多线性回归（MLR）和径向基函数神经网络（RBFNN）进行建模。根据混合物组分的分数浓度计算并定义为复合描述符。由MLR模型提供的统计参数对于训练集为R〜2 = 0.9512，RMS = 0.3792，F = 1402.214和LOO_q〜2 = 0.9462，而R〜2 = 0.9453，RMS = 0.3458，F = 276.671和q_（ ext）〜= 0.9450（用于外部测试集）。 RBFNN模型给出以下统计结果：对于训练集，R〜2 = 0.9779，RMS = 0.2498，F = 3188.202和LOO_q〜2 = 0.9746，R〜2 = 0.9763，RMS = 0.2358，F = 660.631和对于外部测试集，q_（ext）〜2 = 0.9745。总体而言，这些结果表明，基于QSAR MLR的模型是一种用于预测非极性麻醉药品的混合物毒性的简单，可靠，可靠和快速的工具。 RBFNN模型甚至提供了改进的结果。另外，发现ε_（LUMO + 1）（第二低的未占据分子轨道的能量）和PPSA（总电荷加权的部分正表面积）与混合物毒性高度相关。

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