DrugPipe: Generative artificial intelligence-assisted virtual screening pipeline for generalizable and efficient drug repurposing.

IF 1.3 Q3 BIOCHEMICAL RESEARCH METHODS
Biology Methods and Protocols Pub Date : 2025-05-30 eCollection Date: 2025-01-01 DOI:10.1093/biomethods/bpaf038
Phuc Pham, Viet Thanh Duy Nguyen, Kyu Hong Cho, Truong-Son Hy
{"title":"DrugPipe: Generative artificial intelligence-assisted virtual screening pipeline for generalizable and efficient drug repurposing.","authors":"Phuc Pham, Viet Thanh Duy Nguyen, Kyu Hong Cho, Truong-Son Hy","doi":"10.1093/biomethods/bpaf038","DOIUrl":null,"url":null,"abstract":"<p><p>Drug repurposing presents a promising strategy to accelerate drug discovery by identifying new therapeutic uses for existing compounds, particularly for diseases with limited or no effective treatment options. We introduce <b>DrugPipe</b>, a 'Generative AI-Assisted Virtual Screening Pipeline' developed within the target-centric paradigm of drug repurposing, which aims to discover new indications by identifying compounds that interact with a specific protein target. 'DrugPipe' integrates generative modeling, binding pocket prediction, and similarity-based retrieval from drug databases to enable a scalable and generalizable <i>in silico</i> repurposing workflow. It supports blind virtual screening for any protein target without requiring prior structural or functional annotations, making it especially suited for novel or understudied targets and emerging health threats. By efficiently generating candidate ligands and rapidly retrieving structurally similar approved drugs, 'DrugPipe' accelerates the identification and prioritization of repurposable compounds. In comparative evaluations, it achieves hit rate performance comparable to QVina-W, a widely used blind docking tool, while significantly reducing computational time, highlighting its practical value for large-scale virtual screening and data-scarce repurposing scenarios. The full implementation and evaluation details are available at https://github.com/HySonLab/DrugPipe.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf038"},"PeriodicalIF":1.3000,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141201/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biology Methods and Protocols","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/biomethods/bpaf038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Drug repurposing presents a promising strategy to accelerate drug discovery by identifying new therapeutic uses for existing compounds, particularly for diseases with limited or no effective treatment options. We introduce DrugPipe, a 'Generative AI-Assisted Virtual Screening Pipeline' developed within the target-centric paradigm of drug repurposing, which aims to discover new indications by identifying compounds that interact with a specific protein target. 'DrugPipe' integrates generative modeling, binding pocket prediction, and similarity-based retrieval from drug databases to enable a scalable and generalizable in silico repurposing workflow. It supports blind virtual screening for any protein target without requiring prior structural or functional annotations, making it especially suited for novel or understudied targets and emerging health threats. By efficiently generating candidate ligands and rapidly retrieving structurally similar approved drugs, 'DrugPipe' accelerates the identification and prioritization of repurposable compounds. In comparative evaluations, it achieves hit rate performance comparable to QVina-W, a widely used blind docking tool, while significantly reducing computational time, highlighting its practical value for large-scale virtual screening and data-scarce repurposing scenarios. The full implementation and evaluation details are available at https://github.com/HySonLab/DrugPipe.

药物管道:生成人工智能辅助的虚拟筛选管道,用于通用和有效的药物再利用。
药物再利用是一种很有前景的战略,通过确定现有化合物的新治疗用途,特别是对于治疗方案有限或没有有效治疗方案的疾病,可以加速药物的发现。我们介绍了DrugPipe,这是一种在以靶标为中心的药物再利用范例中开发的“生成式人工智能辅助虚拟筛选管道”,旨在通过识别与特定蛋白质靶标相互作用的化合物来发现新的适应症。“DrugPipe”集成了生成建模、绑定口袋预测和基于相似性的药物数据库检索,以实现可扩展和通用的计算机再利用工作流程。它支持对任何蛋白质靶标进行盲虚拟筛选,而不需要事先进行结构或功能注释,使其特别适合于新的或未充分研究的靶标和新出现的健康威胁。通过高效生成候选配体和快速检索结构相似的已批准药物,“DrugPipe”加速了可重复利用化合物的识别和优先排序。在对比评估中,该方法的命中率可与广泛使用的盲对接工具QVina-W相媲美,同时显著减少了计算时间,突出了其在大规模虚拟筛选和数据稀缺再利用场景中的实用价值。完整的实施和评估细节可在https://github.com/HySonLab/DrugPipe上获得。
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
Biology Methods and Protocols
Biology Methods and Protocols Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
3.80
自引率
2.80%
发文量
28
审稿时长
19 weeks
×
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:604180095
Book学术官方微信