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International Journal of Medicinal Chemistry
Volume 2013 (2013), Article ID 743139, 13 pages
Research Article

Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods

1Division of Laboratory Medicine, Zuoying Branch of Kaohsiung Armed Forces General Hospital 813, Kaohsiung 81342, Taiwan
2Department of Life Science, National University of Kaohsiung, Kaohsiung 81148, Taiwan
3Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
4Institute of Biotechnology, National University of Kaohsiung, Kaohsiung 81148, Taiwan

Received 14 September 2012; Accepted 29 March 2013

Academic Editor: Graham B. Jones

Copyright © 2013 Ying-Hsin Chang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Human estrogen receptor (ER) isoforms, ERα and ERβ, have long been an important focus in the field of biology. To better understand the structural features associated with the binding of ERα ligands to ERα and modulate their function, several QSAR models, including CoMFA, CoMSIA, SVR, and LR methods, have been employed to predict the inhibitory activity of 68 raloxifene derivatives. In the SVR and LR modeling, 11 descriptors were selected through feature ranking and sequential feature addition/deletion to generate equations to predict the inhibitory activity toward ERα. Among four descriptors that constantly appear in various generated equations, two agree with CoMFA and CoMSIA steric fields and another two can be correlated to a calculated electrostatic potential of ERα.