The Development of Models Based on Linear and Nonlinear Multivariate Methods to Predict ADME/PK Properties Using Physicochemical Properties of Kinase, Protease Inhibitors, and GPCR Antagonists.

International Journal of Medicinal Chemistry Pub Date : 2013-01-01 Epub Date: 2013-03-19 DOI:10.1155/2013/495134
Deepu Bakasta, M G Shambhu
{"title":"The Development of Models Based on Linear and Nonlinear Multivariate Methods to Predict ADME/PK Properties Using Physicochemical Properties of Kinase, Protease Inhibitors, and GPCR Antagonists.","authors":"Deepu Bakasta, M G Shambhu","doi":"10.1155/2013/495134","DOIUrl":null,"url":null,"abstract":"<p><p>Oral bioavailability of a drug compound is the significant property for potential drug candidates. Measuring this property can be costly and time-consuming. Quantitative structure-property relationships (QSPRs) are used to estimate the percentage of oral bioavailability, and they are an attractive alternative to experimental measurements. A data set of 217 drug and drug-like compounds with measured values of the percentage of oral bioavailability taken from the small molecule ChemBioBase database was used to develop and test a QSPR model. Descriptors were calculated for the compounds using Codessa 2.1 tool. Nonlinear general regression neural network model was generated using the DTREG predictive modeling program software. The calculated percentage of oral bioavailability model performs well, with root-mean-square (rms) errors of 4.55% oral bioavailability units for the training set, 14.32% oral bioavailability units for the test set, and 19.12% oral bioavailability units for the external prediction set. Given the structural diversity and bias of the data set, this is a good first attempt at modeling oral bioavailability using QSPR methods. The model can be used as a potential virtual screen or property estimator. With a larger data supply less biased toward the high end values of the percentage of oral bioavailability, a more successful model could likely be developed. </p>","PeriodicalId":14082,"journal":{"name":"International Journal of Medicinal Chemistry","volume":"2013 ","pages":"495134"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2013/495134","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medicinal Chemistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2013/495134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2013/3/19 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Oral bioavailability of a drug compound is the significant property for potential drug candidates. Measuring this property can be costly and time-consuming. Quantitative structure-property relationships (QSPRs) are used to estimate the percentage of oral bioavailability, and they are an attractive alternative to experimental measurements. A data set of 217 drug and drug-like compounds with measured values of the percentage of oral bioavailability taken from the small molecule ChemBioBase database was used to develop and test a QSPR model. Descriptors were calculated for the compounds using Codessa 2.1 tool. Nonlinear general regression neural network model was generated using the DTREG predictive modeling program software. The calculated percentage of oral bioavailability model performs well, with root-mean-square (rms) errors of 4.55% oral bioavailability units for the training set, 14.32% oral bioavailability units for the test set, and 19.12% oral bioavailability units for the external prediction set. Given the structural diversity and bias of the data set, this is a good first attempt at modeling oral bioavailability using QSPR methods. The model can be used as a potential virtual screen or property estimator. With a larger data supply less biased toward the high end values of the percentage of oral bioavailability, a more successful model could likely be developed.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用激酶、蛋白酶抑制剂和GPCR拮抗剂的理化性质,建立基于线性和非线性多元方法预测ADME/PK特性的模型。
药物化合物的口服生物利用度是潜在候选药物的重要特性。测量该属性既昂贵又耗时。定量结构-性质关系(QSPRs)用于估计口服生物利用度的百分比,它们是一种有吸引力的替代实验测量方法。从ChemBioBase小分子数据库中获取217种药物和类药物化合物的数据集,并测量了口服生物利用度百分比,用于开发和测试QSPR模型。使用Codessa 2.1工具计算化合物的描述符。利用DTREG预测建模程序软件生成非线性一般回归神经网络模型。计算的口服生物利用度百分比模型表现良好,训练集口服生物利用度单位的均方根误差为4.55%,测试集口服生物利用度单位的均方根误差为14.32%,外部预测集口服生物利用度单位的均方根误差为19.12%。考虑到数据集的结构多样性和偏差,这是使用QSPR方法建模口服生物利用度的良好首次尝试。该模型可以用作潜在的虚拟屏幕或属性估计器。有了更大的数据供应,较少偏向于口服生物利用度百分比的高端值,可能会开发出更成功的模型。
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
期刊介绍: International Journal of Medicinal Chemistry is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of chemistry associated with drug discovery, design, and synthesis. International Journal of Medicinal Chemistry is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of chemistry associated with drug discovery, design, and synthesis.
期刊最新文献
Hair Growth Promoting Effect of Dicerocaryum senecioides Phytochemicals. Caffeates and Caffeamides: Synthetic Methodologies and Their Antioxidant Properties Evaluation of the Molecular State of Piperine in Cyclodextrin Complexes by Near-Infrared Spectroscopy and Solid-State Fluorescence Measurements. Synthesis and Evaluation of Baylis-Hillman Reaction Derived Imidazole and Triazole Cinnamates as Antifungal Agents. Crystallography and Its Impact on Carbonic Anhydrase Research.
×
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