• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

用于预测结构多样化学物质雌激素受体结合亲和力的定量构效关系模型。

Quantitative structure-activity relationship models for prediction of estrogen receptor binding affinity of structurally diverse chemicals.

作者信息

Schmieder Patricia K, Ankley Gerald, Mekenyan Ovanes, Walker John D, Bradbury Steven

机构信息

U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Boulevard, Duluth, Minnesota 55804, USA.

出版信息

Environ Toxicol Chem. 2003 Aug;22(8):1844-54. doi: 10.1897/01-345.

DOI:10.1897/01-345
PMID:12924583
Abstract

The demonstrated ability of a variety of structurally diverse chemicals to bind to the estrogen receptor has raised the concern that chemicals in the environment may be causing adverse effects through interference with nuclear receptor pathways. Many structure-activity relationship models have been developed to predict chemical binding to the estrogen receptor as an indication of potential estrogenicity. Models based on either two-dimensional or three-dimensional molecular descriptions that have been used to predict potential for binding to the estrogen receptor are the subject of the current review. The utility of such approaches to predict binding potential of diverse chemical structures in large chemical inventories, with potential application in a tiered risk assessment scheme, is discussed.

摘要

多种结构各异的化学物质已被证实能够与雌激素受体结合,这引发了人们对环境中的化学物质可能通过干扰核受体途径而产生不良影响的担忧。人们已开发出许多构效关系模型,以预测化学物质与雌激素受体的结合情况,作为潜在雌激素活性的指标。基于二维或三维分子描述来预测与雌激素受体结合潜力的模型是本综述的主题。本文还讨论了这些方法在预测大型化学物质清单中各种化学结构的结合潜力方面的实用性,以及其在分层风险评估方案中的潜在应用。

相似文献

1
Quantitative structure-activity relationship models for prediction of estrogen receptor binding affinity of structurally diverse chemicals.用于预测结构多样化学物质雌激素受体结合亲和力的定量构效关系模型。
Environ Toxicol Chem. 2003 Aug;22(8):1844-54. doi: 10.1897/01-345.
2
Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity.两种预测雌激素受体结合活性的构效关系模型的预测置信度评估及领域外推
Environ Health Perspect. 2004 Aug;112(12):1249-54. doi: 10.1289/txg.7125.
3
Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds.(共识)kNN QSAR在预测大量不同有机化合物雌激素活性方面的性能。
SAR QSAR Environ Res. 2004 Feb;15(1):19-32. doi: 10.1080/1062936032000169642.
4
Estrogenic activity assessment of environmental chemicals using in vitro assays: identification of two new estrogenic compounds.使用体外试验评估环境化学物质的雌激素活性:鉴定两种新的雌激素化合物。
Environ Health Perspect. 2000 Jul;108(7):621-9. doi: 10.1289/ehp.00108621.
5
An overview of the use of quantitative structure-activity relationships for ranking and prioritizing large chemical inventories for environmental risk assessments.
Environ Toxicol Chem. 2003 Aug;22(8):1810-21. doi: 10.1897/01-194.
6
Screening of high production volume chemicals for estrogen receptor binding activity (II) by the MultiCASE expert system.利用MultiCASE专家系统筛选高产量化学品的雌激素受体结合活性(II)
Chemosphere. 2003 May;51(6):461-8. doi: 10.1016/S0045-6535(02)00858-5.
7
Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets.基于大数据集的化学物质雌激素受体结合预测决策森林模型的开发与验证
Chem Res Toxicol. 2015 Dec 21;28(12):2343-51. doi: 10.1021/acs.chemrestox.5b00358. Epub 2015 Nov 12.
8
Structure-activity approach to the identification of environmental estrogens: the MCASE approach.用于鉴定环境雌激素的构效关系方法:MC ASE 方法。
SAR QSAR Environ Res. 2004 Feb;15(1):55-67. doi: 10.1080/1062936032000169679.
9
An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity.一种用于确定内分泌干扰物筛选优先级的综合“四阶段”方法——雌激素受体结合亲和力的第一阶段和第二阶段预测。
SAR QSAR Environ Res. 2002 Mar;13(1):69-88. doi: 10.1080/10629360290002235.
10
Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts.使用基于树的模型与结构警示的集成系统预测58000种化学物质的雌激素受体结合情况。
Environ Health Perspect. 2002 Jan;110(1):29-36. doi: 10.1289/ehp.0211029.

引用本文的文献

1
Toward Sustainable Environmental Quality: Priority Research Questions for North America.迈向可持续的环境质量:北美的优先研究问题。
Environ Toxicol Chem. 2019 Aug;38(8):1606-1624. doi: 10.1002/etc.4502.
2
Putative adverse outcome pathways relevant to neurotoxicity.与神经毒性相关的假定不良结局途径。
Crit Rev Toxicol. 2015 Jan;45(1):83-91. doi: 10.3109/10408444.2014.981331.
3
A QSAR study of environmental estrogens based on a novel variable selection method.基于新型变量选择方法的环境雌激素定量构效关系研究。
Molecules. 2012 May 21;17(5):6126-45. doi: 10.3390/molecules17056126.
4
Endocrine profiling and prioritization of environmental chemicals using ToxCast data.利用 ToxCast 数据进行内分泌干扰物的特征分析和优先级排序。
Environ Health Perspect. 2010 Dec;118(12):1714-20. doi: 10.1289/ehp.1002180. Epub 2010 Sep 8.
5
Free energies of ligand binding for structurally diverse compounds.结构多样的化合物的配体结合自由能。
Proc Natl Acad Sci U S A. 2005 May 10;102(19):6750-4. doi: 10.1073/pnas.0407404102. Epub 2005 Mar 14.