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.
Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods
摘要:
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.
English
Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods
Abstract:
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.
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