Letters in Drug Design & Discovery

G. Perry
University of Texas
San Antonio, TX
Email: lddd@benthamscience.org


Prediction of the Estrogen Receptor Binding Affinity for both hERα and hERβ by QSAR Approaches

Author(s): Changliang Deng, Xuanxuan Chen, Hongying Lu, Xin Yang, Feng Luan, Maria Natalia Dias Soeiro Cordeiro.


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.

Keywords: Quantitative Structure-Activity Relationships (QSAR), hERα, hERβ, Multiple Linear Regression (MLR), Radial Basis Function Neural Networks (RBFNN), Binding activity.

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Article Details

Year: 2014
Page: [265 - 278]
Pages: 14
DOI: 10.2174/15701808113109990067
Price: $58