【摘要】 运用多元线性回归（MLR）、非线性支持向量回归（SVR）以及投影寻踪回归（PPR）方法构建了定量结构-活性关系（QSAR）模型，以预测水相有机物与臭氧的反应速率常数(logK_O3).采用内检验和外检验方法，对模型的拟合性能、稳健性以及预测能力进行了比较.结果表明，非线性模型结果优于线性模型；PPR模型的性能最佳.采用SHAP方法对PPR进行可视化表征及分析，以提高模型预测结果的可靠性，增强模型的透明度，从而弥补了机器学习模型的“黑箱”缺陷.最后，利用Williams图法表征了PPR模型的应用域范围（AD）.更多还原

【Abstract】 By using multiple linear regression（MLR）, nonlinear support vector regression（SVR） and projection pursuit regression（PPR） methods, QSAR models were developed to predict the reaction rate constant(logK_O3) of organic compounds with ozone in water. The robustness, fitting performance and ability of the models were compared by internal and external validation procedures. The results showed that, comparatively, nonlinear models were better than linear model, with PPR performs best. Then the PPR model was visualized and explained through SHAP analysis, providing interpretability and explainability for the black-box nature of machine learning model, to strengthen the transparency and credibility of the model. In addition, the applicability domain（AD） range of PPR models was defined and visualized via a Williams plot.更多还原