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个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:日本富山医科药科大学
学位:博士
所在单位:化工学院
学科:药物化学. 药物分析学
办公地点:西部新校区G-302室(制药科学与技术学院)
联系方式:84986195
电子邮箱:zyzhao@dlut.edu.cn
Binding affinity and efficacy-based pharmacophore modeling studies of retinoic acid receptor alpha agonists and virtual screening for potential agonists from NCI
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论文类型:期刊论文
发表时间:2014-08-01
发表刊物:MEDICINAL CHEMISTRY RESEARCH
收录刊物:SCIE、Scopus
卷号:23
期号:8
页面范围:3916-3926
ISSN号:1054-2523
关键字:Retinoic acid receptor alpha; Pharmacophore model; Validation; Virtual screening
摘要:Retinoic acid receptor alpha (RAR alpha) has been considered as one of the most important targets for the treatment of acute promyelocytic leukemia. To discover more novel lead compounds, ligand-based pharmacophore modeling of a series of structurally diverse RAR alpha agonists was applied to acquire the binding model (KI pharmacophore model) and the efficacy model (EC50 pharmacophore model) of RAR alpha. In this paper, a three-dimensional quantitative structure-activity relationship (3D-QSAR) in Discovery Studio 2.5 was used to generate pharmacophore models. Via Fischer's randomization validation and maximum unbiased validation, the best pharmacophore model for KI pharmacophore model was Hypo1K and for EC50 pharmacophore model was Hypo7E. Virtual screening of National Cancer Institute database using Hypo1K and Hypo7E was performed, respectively. Six potent compounds in the retrieved hits with a CAS number were confirmed to be effective on leukemia cell lines and other tumors in the literatures. As evident from the validation and the biological screening results, it can be concluded that the Hypo1K and Hypo7E were reliable and useful tools for lead optimization of novel RAR alpha agonists.
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