• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

计算机药物重定位:我们需要了解的内容。

In silico drug repositioning: what we need to know.

机构信息

ICF International at FDA National Center for Toxicological Research, Jefferson, AR 72079, USA.

出版信息

Drug Discov Today. 2013 Feb;18(3-4):110-5. doi: 10.1016/j.drudis.2012.08.005. Epub 2012 Aug 28.

DOI:10.1016/j.drudis.2012.08.005
PMID:22935104
Abstract

Drug repositioning, exemplified by sildenafil and thalidomide, is a promising way to explore alternative indications for existing drugs. Recent research has shown that bioinformatics-based approaches have the potential to offer systematic insights into the complex relationships among drugs, targets and diseases necessary for successful repositioning. In this article, we propose the key bioinformatics steps essential for discovering valuable repositioning methods. The proposed steps (repurposing with a purpose, repurposing with a strategy and repurposing with confidence) are aimed at providing a repurposing pipeline, with particular focus on the proposed Drugs of New Indications (DNI) database, which can be used alongside currently available resources to improve in silico drug repositioning.

摘要

药物重定位,以西地那非和沙利度胺为例,是探索现有药物新适应症的一种很有前途的方法。最近的研究表明,基于生物信息学的方法有可能为成功的药物重定位提供系统的见解,了解药物、靶点和疾病之间复杂的关系。在本文中,我们提出了发现有价值的药物重定位方法所必需的关键生物信息学步骤。提出的步骤(有目的的重新定位、有策略的重新定位和有信心的重新定位)旨在提供一种重新定位的管道,特别关注提议的新药适应症(DNI)数据库,该数据库可以与现有的资源一起使用,以提高计算机药物重新定位的效果。

相似文献

1
In silico drug repositioning: what we need to know.计算机药物重定位:我们需要了解的内容。
Drug Discov Today. 2013 Feb;18(3-4):110-5. doi: 10.1016/j.drudis.2012.08.005. Epub 2012 Aug 28.
2
Computational Drug Repurposing: Current Trends.计算药物再利用:现状趋势。
Curr Med Chem. 2019;26(28):5389-5409. doi: 10.2174/0929867325666180530100332.
3
Literature mining, ontologies and information visualization for drug repurposing.文献挖掘、本体论和信息可视化在药物重定位中的应用。
Brief Bioinform. 2011 Jul;12(4):357-68. doi: 10.1093/bib/bbr005. Epub 2011 Jun 28.
4
In silico repositioning of approved drugs for rare and neglected diseases.计算机模拟法重新定位已批准药物治疗罕见和被忽视的疾病。
Drug Discov Today. 2011 Apr;16(7-8):298-310. doi: 10.1016/j.drudis.2011.02.016. Epub 2011 Mar 1.
5
Drug repurposing and adverse event prediction using high-throughput literature analysis.药物重定位和基于高通量文献分析的不良事件预测。
Wiley Interdiscip Rev Syst Biol Med. 2011 May-Jun;3(3):323-34. doi: 10.1002/wsbm.147. Epub 2011 Feb 16.
6
Drug repositioning: re-investigating existing drugs for new therapeutic indications.药物重新定位:重新研究现有药物以寻找新的治疗适应症。
J Postgrad Med. 2011 Apr-Jun;57(2):153-60. doi: 10.4103/0022-3859.81870.
7
Mining small-molecule screens to repurpose drugs.挖掘小分子药物筛选库,以重新利用药物。
Brief Bioinform. 2011 Jul;12(4):327-35. doi: 10.1093/bib/bbr028. Epub 2011 Jun 29.
8
In vitro screening for drug repositioning.用于药物重新定位的体外筛选。
J Biomol Screen. 2015 Feb;20(2):167-79. doi: 10.1177/1087057114563024. Epub 2014 Dec 19.
9
A review of validation strategies for computational drug repositioning.计算药物重定位的验证策略综述。
Brief Bioinform. 2018 Jan 1;19(1):174-177. doi: 10.1093/bib/bbw110.
10
Network-based drug repositioning.基于网络的药物重新定位。
Mol Biosyst. 2013 Jun;9(6):1268-81. doi: 10.1039/c3mb25382a. Epub 2013 Mar 14.

引用本文的文献

1
Literature data-based de novo candidates for drug repurposing.基于文献数据的药物重新利用的从头候选药物
BMC Bioinformatics. 2025 Aug 1;26(1):203. doi: 10.1186/s12859-025-06237-7.
2
CPDP: Contrastive Protein-Drug Pre-Training for Novel Drug Discovery.CPDP:用于新药发现的对比蛋白质-药物预训练
Int J Mol Sci. 2025 Apr 16;26(8):3761. doi: 10.3390/ijms26083761.
3
Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines.抗真菌治疗与疫苗研发中的生物信息学方法
Curr Genomics. 2024;25(5):323-333. doi: 10.2174/0113892029281602240422052210. Epub 2024 May 16.
4
Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection.非负矩阵三因子分解在多组学数据集中的表示学习及其在药物重定位和选择中的应用
Int J Mol Sci. 2024 Sep 4;25(17):9576. doi: 10.3390/ijms25179576.
5
FMCA-DTI: a fragment-oriented method based on a multihead cross attention mechanism to improve drug-target interaction prediction.FMCA-DTI:一种基于多头交叉注意力机制的面向片段的方法,用于提高药物-靶标相互作用预测。
Bioinformatics. 2024 Jun 3;40(6). doi: 10.1093/bioinformatics/btae347.
6
Drug-target affinity prediction with extended graph learning-convolutional networks.基于扩展图学习卷积网络的药物-靶标亲和力预测。
BMC Bioinformatics. 2024 Feb 16;25(1):75. doi: 10.1186/s12859-024-05698-6.
7
Finding Lead Compounds for Dengue Antivirals from a Collection of Old Drugs through Target Prediction and Subsequent Validation.通过靶点预测及后续验证从一批旧药中寻找登革热抗病毒先导化合物
ACS Omega. 2023 Aug 28;8(36):32483-32497. doi: 10.1021/acsomega.3c02607. eCollection 2023 Sep 12.
8
OTTM: an automated classification tool for translational drug discovery from omics data.OTTM:一种从组学数据中进行转化药物发现的自动化分类工具。
Brief Bioinform. 2023 Sep 20;24(5). doi: 10.1093/bib/bbad301.
9
DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19.DTSEA:一种基于网络的药物靶标集富集分析方法,用于针对 COVID-19 的药物再利用。
Comput Biol Med. 2023 Jun;159:106969. doi: 10.1016/j.compbiomed.2023.106969. Epub 2023 Apr 21.
10
Potential effect of luteolin, epiafzelechin, and albigenin on rats under cadmium-induced inflammatory insult: and approach.木犀草素、表阿夫儿茶精和白花青素对镉诱导的炎症损伤大鼠的潜在影响:一种体外和体内方法。
Front Chem. 2023 Mar 1;11:1036478. doi: 10.3389/fchem.2023.1036478. eCollection 2023.