Citation: ZHANG Xiao-Long, ZHOU Zhi-Xiang, LIU Yang-Hua, FAN Xue-Lan, LI Han-Dong, WANG Jian-Tao. Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods[J]. Chinese Journal of Structural Chemistry, ;2014, 33(2): 244-252. shu

Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods

1.
a College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China;
2.
b Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
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c Chinese Research Academy of Environmental Sciences, Beijing 100012, China

Received Date: 4 June 2013
Available Online: 22 September 2013
Fund Project: Supported by the Ministry of Environmental Protection of China (No.2011467037) (No.2011467037)

In current paper, a quantitative structure-activity relationship (QSAR) study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression (MLR) and support vector machine (SVM) were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R²=0.71, with higher SVM values of R²=0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.
- aromatic amines,
- acute toxicity,
- quantitative structure-activity relationship (QSAR),
- support vector machine (SVM),
- multiple linear regression (MLR)
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