选择性环氧合酶2 (COX‐2)抑制剂的比较分子场分析

C. Marot, P. Chavatte, D. Lesieur
{"title":"选择性环氧合酶2 (COX‐2)抑制剂的比较分子场分析","authors":"C. Marot, P. Chavatte, D. Lesieur","doi":"10.1002/1521-3838(200004)19:2<127::AID-QSAR127>3.0.CO;2-P","DOIUrl":null,"url":null,"abstract":"The 3D-QSAR approach has been used to obtain informations about the active conformation of selective cyclo-oxygenase-2 (COX-2) inhibitors. In this paper, we have compared different combinations of two fields (steric and electrostatic) in order to optimize the 3D-QSAR models of selective COX-2 inhibitors. Assuming that all the compounds interact at the same binding site at the enzyme level, DuP697 pharmacophoric conformation served as a template for the superimposition of 54 structurally heterogeneous COX-2 inhibitors constituting both the training and test sets used to perform a 3D-QSAR study via the CoMFA method. A statistically significant model was obtained with 38 compounds of the training set (n=38, q2=0,70, N=3, r2=0,93, s=0,38, F=156) with steric and electrostatic relative contributions of 40% and 60%, respectively. The predictive power of the proposed model was discerned by successfully testing the 16 compounds constituting the test set. The so obtained and validated model brings important structural insights to aid the design of novel anti-inflammatory drugs prior to their synthesis.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2000-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Comparative Molecular Field Analysis of Selective Cyclooxygenase‐2 (COX‐2) Inhibitors\",\"authors\":\"C. Marot, P. Chavatte, D. Lesieur\",\"doi\":\"10.1002/1521-3838(200004)19:2<127::AID-QSAR127>3.0.CO;2-P\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The 3D-QSAR approach has been used to obtain informations about the active conformation of selective cyclo-oxygenase-2 (COX-2) inhibitors. In this paper, we have compared different combinations of two fields (steric and electrostatic) in order to optimize the 3D-QSAR models of selective COX-2 inhibitors. Assuming that all the compounds interact at the same binding site at the enzyme level, DuP697 pharmacophoric conformation served as a template for the superimposition of 54 structurally heterogeneous COX-2 inhibitors constituting both the training and test sets used to perform a 3D-QSAR study via the CoMFA method. A statistically significant model was obtained with 38 compounds of the training set (n=38, q2=0,70, N=3, r2=0,93, s=0,38, F=156) with steric and electrostatic relative contributions of 40% and 60%, respectively. The predictive power of the proposed model was discerned by successfully testing the 16 compounds constituting the test set. The so obtained and validated model brings important structural insights to aid the design of novel anti-inflammatory drugs prior to their synthesis.\",\"PeriodicalId\":20818,\"journal\":{\"name\":\"Quantitative Structure-activity Relationships\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantitative Structure-activity Relationships\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/1521-3838(200004)19:2<127::AID-QSAR127>3.0.CO;2-P\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Structure-activity Relationships","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/1521-3838(200004)19:2<127::AID-QSAR127>3.0.CO;2-P","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 26

摘要

3D-QSAR方法已被用于获得选择性环氧化酶-2 (COX-2)抑制剂活性构象的信息。在本文中,我们比较了两种场(空间和静电)的不同组合,以优化选择性COX-2抑制剂的3D-QSAR模型。假设所有化合物在酶水平上在相同的结合位点相互作用,DuP697的药效构象作为54个结构异质COX-2抑制剂的叠加模板,构成了通过CoMFA方法进行3D-QSAR研究的训练集和测试集。训练集的38个化合物(n=38, q2=0,70, n=3, r2=0,93, s=0,38, F=156)的空间和静电相对贡献分别为40%和60%,得到了具有统计学意义的模型。通过成功测试构成测试集的16种化合物来识别所提出模型的预测能力。因此获得和验证的模型带来了重要的结构见解,以帮助在合成之前设计新的抗炎药物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Molecular Field Analysis of Selective Cyclooxygenase‐2 (COX‐2) Inhibitors
The 3D-QSAR approach has been used to obtain informations about the active conformation of selective cyclo-oxygenase-2 (COX-2) inhibitors. In this paper, we have compared different combinations of two fields (steric and electrostatic) in order to optimize the 3D-QSAR models of selective COX-2 inhibitors. Assuming that all the compounds interact at the same binding site at the enzyme level, DuP697 pharmacophoric conformation served as a template for the superimposition of 54 structurally heterogeneous COX-2 inhibitors constituting both the training and test sets used to perform a 3D-QSAR study via the CoMFA method. A statistically significant model was obtained with 38 compounds of the training set (n=38, q2=0,70, N=3, r2=0,93, s=0,38, F=156) with steric and electrostatic relative contributions of 40% and 60%, respectively. The predictive power of the proposed model was discerned by successfully testing the 16 compounds constituting the test set. The so obtained and validated model brings important structural insights to aid the design of novel anti-inflammatory drugs prior to their synthesis.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信