DeepDR用深度学习帮老药找新用途，免编程一键出结果。
DeepDR: an integrated deep-learning model web server for drug repositioning
- 整合多种深度学习模型，统一处理药物重定位任务。
- 基于590万条关系知识图谱，覆盖107类关联数据。
- 无需注册免费使用，适合生物与计算科研人员快速筛选候选药。
背景：为已批准药物寻找新适应症是一项复杂且耗时的过程，需要深厚的药理学、临床数据及先进计算方法知识。近年来，深度学习方法在药物重定位预测中展现出高准确性。然而，实施深度学习建模需深厚领域知识和熟练编程技能。结果：本文介绍DeepDR，首个集成多种成熟深度学习模型的药物重定位平台，支持疾病与靶点特异性任务。DeepDR利用丰富经验推荐候选药物，涵盖超过15个网络和一个综合知识图谱，包含来自6个现有数据库及2400万篇PubMed文献的科学语料，共590万条边，连接药物、疾病、蛋白质/基因、通路和表达数据，涵盖107种关系类型。推荐结果附有药物详细描述，并通过知识图谱可视化关键模式，具备可解释性。结论：DeepDR免费开放，无需注册，可为实验与计算科学家提供易用、系统、高精度、自动化的药物重定位平台。
原文摘要 · Abstract (English)
Background: Identifying new indications for approved drugs is a complex and time-consuming process that requires extensive knowledge of pharmacology, clinical data, and advanced computational methods. Recently, deep learning (DL) methods have shown their capability for the accurate prediction of drug repositioning. However, implementing DL-based modeling requires in-depth domain knowledge and proficient programming skills. Results: In this application, we introduce DeepDR, the first integrated platform that combines a variety of established DL-based models for disease- and target-specific drug repositioning tasks. DeepDR leverages invaluable experience to recommend candidate drugs, which covers more than 15 networks and a comprehensive knowledge graph that includes 5.9 million edges across 107 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from six existing databases and a large scientific corpus of 24 million PubMed publications. Additionally, the recommended results include detailed descriptions of the recommended drugs and visualize key patterns with interpretability through a knowledge graph. Conclusion: DeepDR is free and open to all users without the requirement of registration. We believe it can provide an easy-to-use, systematic, highly accurate, and computationally automated platform for both experimental and computational scientists.
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