Publication: Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study
Issued Date
2018-01-01
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ISSN
20462069
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2-s2.0-85044628264
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Mahidol University
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SCOPUS
Bibliographic Citation
RSC Advances. Vol.8, No.21 (2018), 11344-11356
Suggested Citation
Naravut Suvannang, Likit Preeyanon, Aijaz Ahmad Malik, Nalini Schaduangrat, Watshara Shoombuatong, Apilak Worachartcheewan, Tanawut Tantimongcolwat, Chanin Nantasenamat Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study. RSC Advances. Vol.8, No.21 (2018), 11344-11356. doi:10.1039/c7ra10979b Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/45441
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Title
Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study
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Abstract
© 2018 The Royal Society of Chemistry. Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predisposing factor for the growth of breast cancer cells. As such, ERα represents a lucrative therapeutic target for breast cancer that has attracted wide interest in the search for inhibitory agents. However, the conventional laboratory processes are cost- and time-consuming. Thus, it is highly desirable to develop alternative methods such as quantitative structure-activity relationship (QSAR) models for predicting ER-mediated endocrine agitation as to simplify their prioritization for future screening. In this study, we compiled and curated a large, non-redundant data set of 1231 compounds with ERα inhibitory activity (pIC50). Using comprehensive validation tests, it was clearly observed that the model utilizing the substructure count as descriptors, performed well considering two objectives: using less descriptors for model development and achieving high predictive performance (RTr2 = 0.94, QCV2 = 0.73, and QExt2 = 0.73). It is anticipated that our proposed QSAR model may become a useful high-throughput tool for identifying novel inhibitors against ERα.