BIGDATA Small DA ESCE Interactive and Collaborative On-line virtual Screening

BIGDATA Small DA ESCE 互动协作在线虚拟放映

• 批准号: 8599847
• 负责人: David Ryan Koes
• 金额: $ 19.06万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-10 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8599847
• 关键词: Algorithms; Back; Chemical Structure; Chemicals; Collaborations; Computer software; Computing Methodologies; Data; Databases; Development; Discipline; Environment; Human; Instruction; Investments; Knowledge; Libraries; Methods; Modems; Performance; Pharmaceutical Preparations; Process; Property; Research; Research Infrastructure; Research Personnel; Retrieval; System; Testing; Therapeutic; Time; base; computer infrastructure; computing resources; cost; drug candidate; drug discovery; insight; interest; molecular shape; novel; open source; pharmacophore; screening; tool; virtual

DESCRIPTION (provided by applicant): Chemical space is big data: the number of drug-like molecules exceeds 10^60. Experimentally screening compound libraries for drug candidates is a time consuming and expensive process. Virtual screening is a cheaper, faster approach for identifying potential drug candidates. Existing virtual screening methods typically scale linearly with the size of the compound library. A virtual screen of a million compounds may take days and requires a significant investment in computational infrastructure. The lack of scalable virtual screening algorithms and the difficulty in accessing the infrastructure necessary to perform large-scale virtual screening severely limits the ability of researchers to explore the big data of chemical space. This research plan will develop scalable virtual screening algorithms that will enable virtual screening on an interactive time scale (seconds to minutes). Interactive algorithms support the integration of expert human insight and knowledge with computational methods and permit rapid hypothesis testing and exploration. These interactive algorithms will be deployed both as open-source software and as part of an online drug discovery collaboration environment. The online environment will provide immediate access to the big data infrastructure needed to enable rapid and collaborative online virtual screening. Algorithms for filtering compound libraries based on pharmacophore and molecular shape properties will be developed. Unlike current approaches, these algorithms will scale with the breadth and complexity of the query, not with the size of the compound database, enabling scalable and rapid filtering of billions of chemical structures. Efficient methods for ranking the filtered resuts that harness the computational power of modem graphics processing units will also be developed. Backed by the appropriate computational resources, these algorithms will support the screening of billions of chemical structures on an interactive time-scale. The interactive performance of the tools will support rapid hypothesis testing and experimentation, and users will be able to submit their own compound libraries for screening, encouraging cross-discipline collaboration. RELEVANCE (See instructions): The proposed research will result in novel algorithms and systems for the storage, retrieval, and analysis of chemical data to support the rapid identification of compounds of therapeutic interest. Successful application of these algorithms will reduce the cost and time of development of new drugs.

描述（申请人提供）：化学空间是大数据：类药物分子的数量超过10^60。通过实验筛选候选药物的化合物库是一个耗时且昂贵的过程。虚拟筛选是一种更便宜、更快速的识别潜在候选药物的方法。现有的虚拟筛选方法通常与化合物库的大小成线性比例。对一百万种化合物进行虚拟筛选可能需要数天时间，并且需要对计算基础设施进行大量投资。缺乏可扩展的虚拟 筛选算法以及访问进行大规模虚拟筛选所需的基础设施的困难严重限制了研究人员探索大数据的能力 化学空间。该研究计划将开发可扩展的虚拟筛选算法，该算法将在交互式时间范围（秒到分钟）上实现虚拟筛选。交互式算法支持人类专家洞察力和知识与计算方法的集成，并允许快速假设检验和探索。这些交互式算法将作为开源软件和在线药物发现协作环境的一部分进行部署。在线环境将提供对实现快速协作在线虚拟筛选所需的大数据基础设施的即时访问。将开发基于药效团和分子形状特性的化合物库过滤算法。与当前的方法不同，这些算法将随着查询的广度和复杂性而扩展，而不是随着化合物数据库的大小而扩展，从而实现对数十亿种化学结构的可扩展和快速过滤。还将开发利用现代图形处理单元的计算能力对过滤结果进行排序的有效方法。在适当的计算资源的支持下，这些算法将支持在交互式时间尺度上筛选数十亿种化学结构。这些工具的交互性能将支持快速假设检验和实验，用户将能够提交自己的化合物库进行筛选，鼓励跨学科合作。相关性（参见说明）：拟议的研究将产生用于化学数据存储、检索和分析的新算法和系统，以支持快速识别具有治疗意义的化合物。这些算法的成功应用将减少新药开发的成本和时间。