DAIKON:一个数据采集,集成和知识捕获的Web应用程序,用于基于目标的药物发现
Siddhant Rath1, Saswati Panda1, James C Sacchettini1
1Department of Biochemistry & Biophysics, Texas A&M University, College Station, Texas 77843, United States.
ACS pharmacology & translational science
|July 20, 2023
概括
DAIKON是一个开源框架,旨在统一和管理药物发现数据,改善协作和知识共享. 这个平台增强了多学科团队的数据组织,加速了药物发现管道.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 目前的药物发现数据组织方法不足以进行大规模的多学科合作.
- 有效的数据共享和协调需要一个统一的科学信息收集和分析平台.
研究的目的:
- 介绍DAIKON,一个开源框架,用于整合和管理药物发现项目.
- 在基于目标的药物发现组合中促进协作和知识捕获.
主要方法:
- 戴康集成目标,屏幕和命中,与知识捕获用于分子属性改进.
- 该框架通过讨论线程和视觉进度跟踪支持团队协作.
- 它充当数据存储库,从Mycobrowser,UniProt和PDB获得信息.
- DAIKON是模块化的,具有独特的可扩展性层,并使用Docker进行部署.
主要成果:
- 戴康为药物发现数据管理和协作提供了一个统一的平台.
- 该框架支持全球化药物发现计划,同时尊重当地工作流程的变化.
- 它可以在本地部署或在VPC中部署,并支持Active-Directory/SSO用于用户管理.
- 目前,DAIKON 在结核病药物加速器计划 (TBDA) 中实施.
结论:
- 戴康为药物发现中的数据组织提供了一个可扩展和一致的解决方案.
- 该框架增强了多学科团队的协作,并加速了药物发现过程.
- 它的模块化设计和部署选项使其适应各种组织需求.
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