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Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods
Joint Authors
Chen, Jun-Yan
Yang, Chang-Biau
Chang, Ying-Hsin
Chuang, Yu-Chung
Hor, Chiou-Yi
Yang, Chia-Ning
Source
International Journal of Medicinal Chemistry
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-14
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chang, Ying-Hsin& Chen, Jun-Yan& Hor, Chiou-Yi& Chuang, Yu-Chung& Yang, Chang-Biau& Yang, Chia-Ning. 2013. Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods. International Journal of Medicinal Chemistry،Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-495208
Modern Language Association (MLA)
Chang, Ying-Hsin…[et al.]. Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods. International Journal of Medicinal Chemistry No. 2013 (2013), pp.1-13.
https://search.emarefa.net/detail/BIM-495208
American Medical Association (AMA)
Chang, Ying-Hsin& Chen, Jun-Yan& Hor, Chiou-Yi& Chuang, Yu-Chung& Yang, Chang-Biau& Yang, Chia-Ning. Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods. International Journal of Medicinal Chemistry. 2013. Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-495208
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-495208