2. Academic Validation
  3. Computational study of estrogen receptor-alpha antagonist with three-dimensional quantitative structure-activity relationship, support vector regression, and linear regression methods

Computational study of estrogen receptor-alpha antagonist with three-dimensional quantitative structure-activity relationship, support vector regression, and linear regression methods

  • Int J Med Chem. 2013;2013:743139. doi: 10.1155/2013/743139.
Ying-Hsin Chang 1,  Jun-Yan Chen 2,  Chiou-Yi Hor 3,  Yu-Chung Chuang 4,  Chang-Biau Yang 3,  Chia-Ning Yang 5
Affiliations

Affiliations

  • 1 Division of Laboratory Medicine, Zuoying Branch of Kaohsiung Armed Forces General Hospital 813, Kaohsiung 81342, Taiwan.
  • 2 Department of Life Science, National University of Kaohsiung, Kaohsiung 81148, Taiwan.
  • 3 Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan.
  • 4 Institute of Biotechnology, National University of Kaohsiung, Kaohsiung 81148, Taiwan.
  • 5 Department of Life Science, National University of Kaohsiung, Kaohsiung 81148, Taiwan ; Institute of Biotechnology, National University of Kaohsiung, Kaohsiung 81148, Taiwan.
PMID: 25505989 DOI: 10.1155/2013/743139
Abstract

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.

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