Tugcu Gulcin, Yilmaz H Birkan, Saçan Melek Türker
Institute of Environmental Sciences, Bogazici University, 34342, Bebek, Istanbul, Turkey.
Environ Sci Pollut Res Int. 2014 Oct;21(20):11924-32. doi: 10.1007/s11356-014-3182-3. Epub 2014 Jun 21.
This study presents quantitative structure-toxicity relationship (QSTR) models on the toxicity of 91 organic compounds to Chlorella vulgaris using multiple linear regression (MLR) and Kriging techniques. The molecular descriptors were calculated using SPARTAN and DRAGON programs, and descriptor selection was made by "all subset" method available in the QSARINS software. MLR and Kriging models developed with the same descriptors were compared. In addition to these models, Kriging method was used for descriptor selection, and model development. The selected descriptors showed the importance of hydrophobicity, molecular weight and atomic ionization state in describing the toxicity of a diverse set of chemicals to C. vulgaris. A QSTR model should be associated with appropriate measures of goodness-of-fit, robustness, and predictivity in order to be used for regulatory purpose. Therefore, while the internal performances (goodness-of-fit and robustness) of the models were determined by using a training set, the predictive abilities of the models were determined by using a test set. The results of the study showed that while MLR method is easier to apply, the Kriging method was more successful in predicting toxicity.
本研究运用多元线性回归（MLR）和克里金技术，建立了91种有机化合物对普通小球藻毒性的定量结构-毒性关系（QSTR）模型。使用SPARTAN和DRAGON程序计算分子描述符，并通过QSARINS软件中可用的“所有子集”方法进行描述符选择。比较了使用相同描述符开发的MLR和克里金模型。除了这些模型外，还使用克里金方法进行描述符选择和模型开发。所选描述符表明了疏水性、分子量和原子电离状态在描述多种化学品对普通小球藻毒性方面的重要性。为了用于监管目的，QSTR模型应与适当的拟合优度、稳健性和预测性度量相关联。因此，在使用训练集确定模型的内部性能（拟合优度和稳健性）的同时，使用测试集确定模型的预测能力。研究结果表明，虽然MLR方法更易于应用，但克里金方法在预测毒性方面更为成功。
