Abstract
Estrogens play a crucial role in the growth, development, and homeostasis of various target tissues, their biological effects being mediated by the estrogen receptor (ER). In order to get a better understanding of the structural features of the modulators associated with the binding to ER, this paper provides an overview of the Quantitative Structure–Activity (QSAR) studies performed so far for estimating or predicting the activity of different ligands towards its two known subtypes (ERα and ERβ). Recent progresses in the application of these modeling studies are additionally pointed out. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling studies in this field are discussed.
Keywords: Estrogen receptor, QSAR modeling, virtual screening, multi-task learning, ERα, ERβ.
Current Drug Targets
Title:Rational Design of Multi-Target Estrogen Receptors ERα and ERβ by QSAR Approaches
Volume: 18 Issue: 5
Author(s): Qi Zhao, Yuxi Lu, Yan Zhao, Rongchao Li, Feng Luan and M. Natalia D.S. Cordeiro
Affiliation:
Keywords: Estrogen receptor, QSAR modeling, virtual screening, multi-task learning, ERα, ERβ.
Abstract: Estrogens play a crucial role in the growth, development, and homeostasis of various target tissues, their biological effects being mediated by the estrogen receptor (ER). In order to get a better understanding of the structural features of the modulators associated with the binding to ER, this paper provides an overview of the Quantitative Structure–Activity (QSAR) studies performed so far for estimating or predicting the activity of different ligands towards its two known subtypes (ERα and ERβ). Recent progresses in the application of these modeling studies are additionally pointed out. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling studies in this field are discussed.
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Cite this article as:
Zhao Qi, Lu Yuxi, Zhao Yan, Li Rongchao, Luan Feng and Cordeiro Natalia D.S. M., Rational Design of Multi-Target Estrogen Receptors ERα and ERβ by QSAR Approaches, Current Drug Targets 2017; 18 (5) . https://dx.doi.org/10.2174/1389450117666160401125542
DOI https://dx.doi.org/10.2174/1389450117666160401125542 |
Print ISSN 1389-4501 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-5592 |
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