Estimating the Strength of Binding Affinity via Delta-Delta-G for Hit Screening After a Deming Regression Calibration.

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2025-01-01 DOI:10.1002/pst.2460
Kanaka Tatikola, Javier Cabrera
{"title":"Estimating the Strength of Binding Affinity via Delta-Delta-G for Hit Screening After a Deming Regression Calibration.","authors":"Kanaka Tatikola, Javier Cabrera","doi":"10.1002/pst.2460","DOIUrl":null,"url":null,"abstract":"<p><p>In compound hit screening, an important chemical property is target binding affinity, represented by a parameter ΔΔG. You can measure ΔΔG experimentally (ΔΔG<sub>exp</sub>) or by calculations via simulations (ΔΔG<sub>calc</sub>). Because it is expensive to measure ΔΔG experimentally, only a few experimental runs are performed. The relationship between the experimental data and the calculated results is a straight line with a slope that is not necessarily one. The goal is to estimate the linear relationship between ΔΔG<sub>exp</sub> and ΔΔG<sub>calc</sub> by fitting a Deming regression model that will be used to predict future values of ΔΔG<sub>true</sub> based on the obtained ΔΔG<sub>calc</sub>.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 1","pages":"e2460"},"PeriodicalIF":1.3000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pst.2460","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

Abstract

In compound hit screening, an important chemical property is target binding affinity, represented by a parameter ΔΔG. You can measure ΔΔG experimentally (ΔΔGexp) or by calculations via simulations (ΔΔGcalc). Because it is expensive to measure ΔΔG experimentally, only a few experimental runs are performed. The relationship between the experimental data and the calculated results is a straight line with a slope that is not necessarily one. The goal is to estimate the linear relationship between ΔΔGexp and ΔΔGcalc by fitting a Deming regression model that will be used to predict future values of ΔΔGtrue based on the obtained ΔΔGcalc.

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
求助全文
约1分钟内获得全文 去求助
来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
期刊最新文献
A Federated Data Analysis Approach for the Evaluation of Surrogate Endpoints. Approximate Bayesian Analysis for Borrowing External Controls for Randomized Controlled Trials With Dynamic Borrowing and Covariate Balancing Adjustment. Trial Probability of Success for Testing 3-Way PK/PD Similarity With Multiple Endpoints. Introduction to qualification and validation of an immunoassay. What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing.
×
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