Qsar Analysis of the Estrogen Activities of Bisphenol a Analogs using Multiple Linear Regression and Artificial Neural Network
Authors: Van Dat Vu , Long Le Kim , Phuc Doan Van , Trang Nguyen Hoang , Trang Nguyen Van 
Published: June 2019

Abstract. This study gives a quantitative structure-activity relationship (QSAR) analysis of the estrogen activities of bisphenol A and analogs. The chemical structures of 23 Bisphenol A analogs have been characterized by quantum-electronic descriptors. The present study was performed using multiple regression analysis (MLR) and artificial neural network (ANN). The quantitative model was accordingly proposed and the toxicity of the compounds was interpreted based on the multivariate statistical analysis. This study shows that the results obtained by MLR were suitable and have served to predict estrogen activities, but compared to the results of the ANN model, we conclude that the prediction achieved by the latter is more effective and better than MLR model. Following to the obtained results, our proposed model may be useful to predict of Estrogen activities and risk assessment of chemicals.
Keywords
QSAR, Bisphenol A, multiple linear regression, artificial neural network
References
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