Quantitative Structure-Activity Relationship Modeling and Bayesian Networks: Optimality of Naive Bayes Model

O. Kupervasser
{"title":"Quantitative Structure-Activity Relationship Modeling and Bayesian Networks: Optimality of Naive Bayes Model","authors":"O. Kupervasser","doi":"10.5772/INTECHOPEN.85976","DOIUrl":null,"url":null,"abstract":"Previously, computational drag design was usually based on simplified laws of molecular physics, used for calculation of ligand ’ s interaction with an active site of a protein-enzyme. However, currently, this interaction is widely estimated using some statistical properties of known ligand-protein complex properties. Such statistical properties are described by quantitative structure-activity relationships (QSAR). Bayesian networks can help us to evaluate stability of a ligand-protein complex using found statistics. Moreover, we are possible to prove optimality of Naive Bayes model that makes these evaluations simple and easy for practical realization. We prove here optimality of Naive Bayes model using as an illustration ligand-protein interaction.","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bayesian Networks - Advances and Novel Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/INTECHOPEN.85976","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

Previously, computational drag design was usually based on simplified laws of molecular physics, used for calculation of ligand ’ s interaction with an active site of a protein-enzyme. However, currently, this interaction is widely estimated using some statistical properties of known ligand-protein complex properties. Such statistical properties are described by quantitative structure-activity relationships (QSAR). Bayesian networks can help us to evaluate stability of a ligand-protein complex using found statistics. Moreover, we are possible to prove optimality of Naive Bayes model that makes these evaluations simple and easy for practical realization. We prove here optimality of Naive Bayes model using as an illustration ligand-protein interaction.
定量构效关系建模与贝叶斯网络:朴素贝叶斯模型的最优性
以前,计算阻力设计通常基于简化的分子物理定律,用于计算配体与蛋白酶活性位点的相互作用。然而,目前,这种相互作用被广泛地使用已知配体-蛋白质复合物性质的一些统计性质来估计。这种统计性质是用定量构效关系(QSAR)来描述的。贝叶斯网络可以帮助我们利用已知的统计量来评估配体-蛋白质复合物的稳定性。此外,我们可以证明朴素贝叶斯模型的最优性,使这些评估简单,易于实际实现。本文以配体-蛋白相互作用为例,证明了朴素贝叶斯模型的最优性。
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