基于结构的雌激素受体 β 配体定量构效关系建模。

Structure-based quantitative structure--activity relationship modeling of estrogen receptor β-ligands.

机构信息

Department of Pharmaceutical Sciences, College of Science & Technology and BRITE Institute, North Carolina Central University, Durham, NC 27707, USA.

出版信息

Future Med Chem. 2011 Jun;3(8):933-45. doi: 10.4155/fmc.11.49.

DOI:10.4155/fmc.11.49

PMID:21707397

Abstract

BACKGROUND

A variety of chemotypes have been studied as estrogen receptor (ER) β-selective ligands for potential drugs against various indications, including neurodegenerative diseases. Their structure--activity relationship data and the x-ray structures of the ERβ ligand-binding domain bound with different ligands have become available. Thus, it is vitally important for future development of ERβ-selective ligands that robust quantitative structure-activity relationship (QSAR) models be built.

METHODS/RESULTS: We employed a newly developed structure--based QSAR method (structure-based pharmacophore keys QSAR) that utilizes both the structure--activity relationship data and the 3D structural information of ERβ, as well as a robust QSAR workflow to analyze 37 ligands. Four sets of QSAR models were obtained, among which approximately 30 models afforded high (>0.60) training-r(2) and test set-R(2) statistics.

CONCLUSION

We have obtained an ensemble of predictive models of ERβ ligands that will be useful in the future discovery of novel ERβ-selective molecules.

摘要

背景

为了寻找针对各种适应症（包括神经退行性疾病）的潜在药物，人们研究了多种化学型作为雌激素受体 (ER)β 选择性配体。现已获得其结构-活性关系数据和与不同配体结合的 ERβ 配体结合域的 X 射线结构。因此，为了未来开发 ERβ 选择性配体，建立强大的定量构效关系 (QSAR) 模型至关重要。

方法/结果：我们采用了一种新开发的基于结构的 QSAR 方法（基于结构的药效团关键 QSAR），该方法既利用了 ERβ 的结构-活性关系数据和 3D 结构信息，又利用了强大的 QSAR 工作流程来分析 37 种配体。得到了四组 QSAR 模型，其中约 30 个模型的训练-R2 和测试集-R2 统计数据较高（>0.60）。