Prediction of the Estrogen Receptor Binding Affinity for both hERα and hERβ by QSAR Approaches
Changliang Deng, Xuanxuan Chen, Hongying Lu, Xin Yang, Feng Luan and Maria Natalia Dias Soeiro Cordeiro
Affiliation: Center of Green Chemistry, Department of Applied Chemistry, Yantai University, Yantai 264005, P. R. China.
Keywords: Quantitative Structure-Activity Relationships (QSAR), hERα, hERβ, Multiple Linear Regression (MLR), Radial
Basis Function Neural Networks (RBFNN), Binding activity.
A QSAR study is reported to predict the binding affinity of a set of 81 modulators for both of human estrogen
receptor α and β (ERα and ERβ). In this study, the derived QSAR models were built by forward stepwise multilinear regression
(MLR) and nonlinear radial basis function neural networks (RBFNN), respectively. The statistical characteristics
of the external test set provided by multiple linear model (R2=0.814, F=61.277, RMS=0.5461 for ERα; R2=0.600,
F=21.039, RMS=0.6707 for ERβ) indicated satisfactory stability and predictive ability of the model built. The predictive
ability for ERβ of RBFNN model is somewhat superior: R2=0.7691, F=32.012, RMS=0.5764, and the similar result was
obtained for ERα of the test set: R2=0.7950, F=54.131 RMS=0.3120. Overall, the appropriate results proved the models to
be meaningful and useful to predict and virtual screen of the derivatives with high binding activity.
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