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: 10484172
• 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/10484172
• 关键词: Address; Adoption; Age; Binding; Biological; Businesses; Cellular Phone; Chemicals; Cloud Service; Computational algorithm; Computer software; Custom; Data; Data Analyses; Data Set; Data Sources; Databases; Development; Disease; Distributed Databases; Effectiveness; Environment; Informatics; Intuition; Marketing; Methodology; Modeling; Modernization; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Privatization; Problem Solving; 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 exchange; 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.

项目概要 Collaborative Drug Discovery, Inc. (CDD) 提议开发药物发现云工作空间 – 新颖的信息学框架将使科学家能够从事药物发现和翻译工作- 更少、稳健、安全地集成分布在多个领域的不同数据库和计算工具 系统和供应商进入高效、定制的计算工作流程。 ogy 将解决阻碍药物发现和转化工作的关键问题：该领域的科学家通常 特别需要结合多个来源的化学和生物数据，通过多个软件运行它们 专门从事不同类型的分析和可视化的软件包，然后理想地存储结果 如今，这种类型的整合非常困难且困难。 实现起来成本高昂且通常很脆弱，这对 (i) 利用快速增长的资源造成了巨大障碍 高质量的公共访问数据存储库的数量，以及（ii）评估有前途的新分析工具和 整体式平台可以通过将所有内容集中在一个屋檐下来解决这一问题，但是。 它们非常昂贵并且限制了灵活性：没有一个平台可以提供所有功能。 本机方法（将离散资源串在一起）是在桌面计算时代发展起来的 不能很好地适应现代基于云的工作流程，特别是执行的挑战 需要组合分布在远程系统上的大型数据集的计算密集型操作。 云工作空间 (CW) 旨在结合这两个极端的优点并避免其缺点。 从本质上讲，用户可以轻松创建适合自己的个性化云托管解决方案 我们的方法提供了性能、稳健性和易用性。 整体软件解决方案，但没有相关的不灵活性和供应商锁定。它提供了灵活性 以及组合离散资源的开放性，但没有相关的集成挑战和脆弱性， 它推进了管道方法以采用基于云的模型并包含分布式数据 在不影响性能或安全性的情况下，我们证明了我们可以稳健地利用资源。 有效同步生物和化学数据（仅传输新的或修改的数据，同时保留 化学标识符的正确关联） CW 容器环境和远程数据库之间， 这对于我们的概念来说是一个具有挑战性但必不可少的先决条件，我们将在第二阶段完成开发。 CW 并通过多个实际应用程序和软件应用程序证明其有效性 该技术的市场范围从学术界到小型和小型企业。 从中型公司到大型制药公司。