A Robust, Secure Framework to Effortlessly Bind Distributed Databases and Analysis Tools into Tightly Integrated Translational Drug Discovery Computational Platforms

一个强大、安全的框架，可以轻松地将分布式数据库和分析工具绑定到紧密集成的转化药物发现计算平台中

• 批准号: 10685358
• 负责人: BARRY A BUNIN
• 金额: $ 85.49万
• 依托单位: COLLABORATIVE DRUG DISCOVERY, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10685358
• 关键词: Acceleration; Address; Adoption; Binding; Biological; Businesses; Cellular Phone; Chemicals; Cloud Computing; Cloud Service; Computational algorithm; Computer software; Custom; Data; Data Analyses; Data Set; Data Sources; Databases; Development; Disease; Disparate; Distributed Databases; Effectiveness; Environment; Informatics; Intuition; Marketing; Methodology; Modeling; Modernization; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Privatization; Process; Provider; PubChem; Public Domains; Research; Resources; Running; Scientist; Secure; Security; Source; Specific qualifier value; System; Techniques; Technology; Testing; Translations; United States National Institutes of Health; Vendor; Visual; Visualization; analytical tool; chemical association; cloud based; computational platform; computer framework; computerized tools; data access; data repository; data resource; distributed data; drug discovery; flexibility; improved; innovation; innovative technologies; large datasets; multiple data sources; novel; open source; operation; prevent; real world application; tool; virtual environment

PROJECT SUMMARY Collaborative Drug Discovery, Inc. (CDD) proposes to develop Cloud Workspaces for Drug Discovery – a novel informatics framework that will enable scientists engaged in drug discovery and translation to effort- lessly, robustly, and securely integrate disparate databases and computational tools distributed across multiple systems and vendors into highly-efficient, custom-tailored computational workflows. Our innovative technol- ogy will solve a critical problem that hinders drug discovery and translation efforts: scientists in this field typi- cally need to combine chemical and biological data from several sources, run them through multiple software packages that specialize in different types of analyses and visualization, then ideally store the results of the analyses together with their underlying experimental data. Today, this type of integration is difficult and expensive to accomplish and typically fragile, creating a large barrier to (i) exploiting the rapidly increasing number of high-quality public-access data repositories and (ii) evaluating promising new analytical tools and strategies. Monolithic platforms offer to solve this problem by bringing everything together under one roof, but they are extremely expensive and they limit flexibility: no single platform can offer every capability. The alter- native approach – stringing together discrete resources – evolved during the era of desktop computing and does not translate well to modern cloud-based workflows and in particular to the challenges of performing computationally intensive operations that require combining large datasets distributed across remote systems. Cloud Workspaces (CW) aims to combine the strengths and avoid the weaknesses of these two extremes. CW will in essence allow users to easily create their own individualized cloud-hosted solutions tailored to their unique requirements and workflows. Our approach offers the performance, robustness, and ease of use of a monolithic software solution, but without the associated inflexibility and vendor lock in. It offers the flexibility and openness of combining discrete resources, but without the associated integration challenges and fragility, and it advances the pipelining approach to embrace cloud-based models and to encompass distributed data resources without compromising performance or security. In Phase 1 we proved that we could robustly and efficiently synchronize biological and chemical data (transferring only new or modified data while retaining correct association of chemical identifiers) between the CW container environment and remote databases, which was a challenging but essential prerequisite for our concept. In Phase 2 we will complete development of CW and demonstrate its effectiveness with multiple real-world applications together with software application partners and beta customer end users. The market for the technology ranges from academics to small and medium size companies to the large pharmaceutical firms.

项目总结