Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

E. Hadaji, M. Bourass, A. Ouammou, M. Bouachrine
{"title":"Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study","authors":"E. Hadaji, M. Bourass, A. Ouammou, M. Bouachrine","doi":"10.1155/2017/7629056","DOIUrl":null,"url":null,"abstract":"(E)-N-Aryl-2-ethene-sulfonamide and its derivatives are potent anticancer agents; these compounds inhibit cancer cells proliferation. A study of quantitative structure-activity relationship (QSAR) has been applied on 40 compounds based on (E)-N-Aryl-2-ethene-sulfonamide, in order to predict their anticancer biological activity. The principal components analysis is used for minimizing the base matrix and the multiple linear regression (MLR) and multiple nonlinear regression have been used to design the relationships between the molecular descriptor and anticancer properties of the sulfonamide derivatives. The validation of the models MLR and MNLR has been done by dividing the dataset into training and test set, the external validation of multiple correlation coefficients was RpIC50 = 0.81 for MLR and RpIC50 = 0.91 for MNLR. The artificial neural network (ANN) showed a correlation coefficient close to 0.96, which concluded that this latter model is more effective and much better than the other models. This obtained model (ANN) has been confirmed by two methods of LOO cross-validation and scrambling (or Y-randomization). The high correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR model.","PeriodicalId":7371,"journal":{"name":"Advances in Physical Chemistry","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Physical Chemistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2017/7629056","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

(E)-N-Aryl-2-ethene-sulfonamide and its derivatives are potent anticancer agents; these compounds inhibit cancer cells proliferation. A study of quantitative structure-activity relationship (QSAR) has been applied on 40 compounds based on (E)-N-Aryl-2-ethene-sulfonamide, in order to predict their anticancer biological activity. The principal components analysis is used for minimizing the base matrix and the multiple linear regression (MLR) and multiple nonlinear regression have been used to design the relationships between the molecular descriptor and anticancer properties of the sulfonamide derivatives. The validation of the models MLR and MNLR has been done by dividing the dataset into training and test set, the external validation of multiple correlation coefficients was RpIC50 = 0.81 for MLR and RpIC50 = 0.91 for MNLR. The artificial neural network (ANN) showed a correlation coefficient close to 0.96, which concluded that this latter model is more effective and much better than the other models. This obtained model (ANN) has been confirmed by two methods of LOO cross-validation and scrambling (or Y-randomization). The high correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR model.
基于(E)- n -芳基-2-乙烯-磺酰胺的有机化合物作为前列腺癌微管靶向药物的QSAR研究
(E)- n -芳基-2-乙烯磺酰胺及其衍生物是有效的抗癌剂;这些化合物抑制癌细胞的增殖。应用定量构效关系(QSAR)研究了40个以(E)- n -芳基-2-乙烯-磺酰胺为基础的化合物,以预测其抗癌生物活性。采用主成分分析最小化基矩阵,采用多元线性回归和多元非线性回归设计了分子描述符与磺胺类衍生物抗癌性能之间的关系。将数据集分为训练集和测试集,对MLR和MNLR模型进行验证,多相关系数的外部验证为MLR的RpIC50 = 0.81, MNLR的RpIC50 = 0.91。人工神经网络(ANN)的相关系数接近0.96,表明后一种模型比其他模型更有效,效果更好。所得到的模型(ANN)通过LOO交叉验证和置乱(或y随机化)两种方法进行了验证。实验值与预测值高度相关,表明推导的QSAR模型的有效性和质量良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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