Comparison of QSAR models based on combinations of genetic algorithm, stepwise multiple linear regression, and artificial neural network methods to predict Kd of some derivatives of aromatic sulfonamides as carbonic anhydrase II inhibitors.

Bioorganicheskaia khimiia Pub Date : 2014-01-01
Afshin Maleki, Hiua Daraei, Loghman Alaei, Aram Faraji
{"title":"Comparison of QSAR models based on combinations of genetic algorithm, stepwise multiple linear regression, and artificial neural network methods to predict Kd of some derivatives of aromatic sulfonamides as carbonic anhydrase II inhibitors.","authors":"Afshin Maleki,&nbsp;Hiua Daraei,&nbsp;Loghman Alaei,&nbsp;Aram Faraji","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (Kd) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.</p>","PeriodicalId":9325,"journal":{"name":"Bioorganicheskaia khimiia","volume":"40 1","pages":"70-84"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioorganicheskaia khimiia","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (Kd) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.

分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于遗传算法、逐步多元线性回归和人工神经网络方法联合预测芳香磺胺类碳酸酐酶II抑制剂衍生物Kd的QSAR模型比较
采用4种逐步多元线性回归(SMLR)和基于遗传算法(GA)的多元线性回归(MLR),结合人工神经网络(ANN)模型,对62种ArSA衍生物作为人碳酸酐酶II (HCA II)抑制剂的解离常数(Kd)进行定量构效关系(QSAR)建模。采用SMLR和GA-MLR两种方法筛选出最佳的分子描述子子集。这些选定的变量被用来生成MLR和ANN模型。通过外部测试集和交叉验证来检验模型的可预测性。此外,还进行了一些测试来检查模型的其他方面。结果表明,对于某些目的,GA-MLR优于SMLR,而对于其他目的,ANN则优于MLR模型。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
ONE-POT THREE-COMPONENT MICROWAVE-ASSISTED SYNTHESIS OF NOVEL THIAZOLIDINONE DERIVATIVES CONTAINING THIENO[d]PYRIMIDINE-4-ONE MOIETY AS POTENTIAL ANTIMICROBIAL AGENTS. SYNTHESIS AND BIOLOGICAL EVALUATION OF NOVEL COUMARIN DERIVATIVES AS ANTIOXIDANT AGENTS. SYNTHESIS AND IN VITRO ANTIMICROBIAL EVALUATION OF PIPERAZINE SUBSTITUTED QUINAZOLINE-BASED THIOUREA/THIAZOLIDINONE/CHALCONE HYBRIDS. SYNTHESIS AND BIOLOGICAL EVALUATION OF N-(SUBSTITUTED PHENYL)-2-(5H-[1,2,4]TRIAZINO[5,6-b]INDOL-3-YLSULFANYL)ACETAMIDES AS ANTIMICROBIAL, ANTIDEPRESSANT AND ANTICONVULSANT AGENTS. [Creation and Study of Triterpenoid Nanoparticles and Amphiphilic meso-Arylporphyrins].
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1