QSAR and Docking Studies of Thiazolidine-4-carboxylic Acid Derivatives as Neuraminidase Inhibitors
仝建波;王天浩;吴英纪;冯怡
a (College of Chemistry and Chemical Engineering,Shaanxi University of Science and Technology, Xi’an 710021, China)
b (Shaanxi Key Laboratory of Chemical Additives for Industry, Xi’an 710021, China)
QSAR and Docking Studies of Thiazolidine-4-carboxylic Acid Derivatives as Neuraminidase Inhibitors
TONG Jian-Bo;WANG Tian-Hao;WU Ying-Ji;FENG Yi
a (College of Chemistry and Chemical Engineering,Shaanxi University of Science and Technology, Xi’an 710021, China)
b (Shaanxi Key Laboratory of Chemical Additives for Industry, Xi’an 710021, China)
In order to understand the chemical-biological interactions governing their activi- ties toward neuraminidase (NA), QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. Here a quantitative structure activity relationship (QSAR) model was built by three-dimensional holographic atomic vector field (3D-HoVAIF) and multiple linear regression (MLR). The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations. The correlation coefficient (R2) of established MLR model was 0.984, and the cross-validated correlation coefficient (Q2) of MLR model was 0.947. Furthermore, the cross-validated correlation coefficient for the test set (Qext2) was 0.967. The binding mode pattern of the compounds to the binding site of integrase enzyme was confirmed by docking studies. The results of present study indicated that this model can aid in designing more potent neuraminidase inhibitors.
In order to understand the chemical-biological interactions governing their activi- ties toward neuraminidase (NA), QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. Here a quantitative structure activity relationship (QSAR) model was built by three-dimensional holographic atomic vector field (3D-HoVAIF) and multiple linear regression (MLR). The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations. The correlation coefficient (R2) of established MLR model was 0.984, and the cross-validated correlation coefficient (Q2) of MLR model was 0.947. Furthermore, the cross-validated correlation coefficient for the test set (Qext2) was 0.967. The binding mode pattern of the compounds to the binding site of integrase enzyme was confirmed by docking studies. The results of present study indicated that this model can aid in designing more potent neuraminidase inhibitors.
基金资助:This work was supported by the National Natural Science Funds of China (21475081), the Natural Science Foundation of Shaanxi Province (2019JM-237), and the Graduate Innovation Fund of Shaanxi University of Science and Technology
仝建波;王天浩;吴英纪;冯怡. QSAR and Docking Studies of Thiazolidine-4-carboxylic Acid Derivatives as Neuraminidase Inhibitors[J]. 结构化学, 2020, 39(4): 651-661.
TONG Jian-Bo;WANG Tian-Hao;WU Ying-Ji;FENG Yi. QSAR and Docking Studies of Thiazolidine-4-carboxylic Acid Derivatives as Neuraminidase Inhibitors. CHINESE JOURNAL OF STRUCTURAL CHEMISTRY, 2020, 39(4): 651-661.
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2020, Vol. 39 
