计算机辅助模式评分(C@PS):预测具有罕见作用模式配体的新型化学信息学工作流程

IF 7.1 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Journal of Cheminformatics Pub Date : 2024-09-23 DOI:10.1186/s13321-024-00901-5
Sven Marcel Stefan, Katja Stefan, Vigneshwaran Namasivayam
{"title":"计算机辅助模式评分(C@PS):预测具有罕见作用模式配体的新型化学信息学工作流程","authors":"Sven Marcel Stefan,&nbsp;Katja Stefan,&nbsp;Vigneshwaran Namasivayam","doi":"10.1186/s13321-024-00901-5","DOIUrl":null,"url":null,"abstract":"<div><p>The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, <i>e.g.</i>, inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (<i>e.g.</i>, channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (<i>e.g.</i>, inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":7.1000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00901-5","citationCount":"0","resultStr":"{\"title\":\"Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action\",\"authors\":\"Sven Marcel Stefan,&nbsp;Katja Stefan,&nbsp;Vigneshwaran Namasivayam\",\"doi\":\"10.1186/s13321-024-00901-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, <i>e.g.</i>, inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (<i>e.g.</i>, channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (<i>e.g.</i>, inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability.</p></div>\",\"PeriodicalId\":617,\"journal\":{\"name\":\"Journal of Cheminformatics\",\"volume\":\"16 1\",\"pages\":\"\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00901-5\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cheminformatics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1186/s13321-024-00901-5\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cheminformatics","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1186/s13321-024-00901-5","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

潜在药理药物靶点的识别、确立和探索是药物开发流程的主要步骤。靶点验证需要多种化学工具,如抑制剂、激活剂和其他调节剂等。特别是具有罕见作用模式的工具,可以对感兴趣的靶点(如通道、酶、受体或转运体)进行适当的动力学和功能表征。尽管功能创新是获得专利和商业开发的首要标准,但这可能会带来治疗效果。遗憾的是,有关新靶点的数据非常稀少,因此也就无法用药或几乎无法用药,而且大多只有主流作用方式(如抑制)的数据。在此,我们介绍一种新颖的化学信息学工作流程--计算机辅助模式评分(C@PS)--该流程专门设计用于将其预测能力投射到一个未知的适用领域。所介绍的工作流程首次解决了数据稀缺的难题,尤其是针对罕见作用模式。此外,该工作流程和相关数据集为合理选择候选药物的标准定义和应用提供了新的标准,解决了化学信息学、计算化学和药物化学领域的重要空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action

The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (e.g., inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
期刊最新文献
GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts Suitability of large language models for extraction of high-quality chemical reaction dataset from patent literature Molecular identification via molecular fingerprint extraction from atomic force microscopy images A systematic review of deep learning chemical language models in recent era Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1
×
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