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（由申请人提供）：化学空间是大数据：类似药物的分子数量超过10^60。候选药物的实验筛选化合物库是一个耗时且昂贵的过程。虚拟筛查是识别潜在候选药物的便宜，更快的方法。现有的虚拟筛选方法通常与复合库的大小线性扩展。 100万种化合物的虚拟屏幕可能需要几天，并且需要对计算基础架构进行大量投资。缺乏可扩展的虚拟 筛选算法和访问进行大规模虚拟筛查所需的基础架构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，这些算法将在交互式时间尺度（秒至分钟）上进行虚拟筛选。交互式算法支持专家人类洞察力和知识与计算方法的整合，并允许快速的假设检验和探索。这些交互式算法将既将作为开源软件，也是在线药物发现协作环境的一部分。在线环境将立即访问启用快速和协作的在线虚拟筛查所需的大数据基础架构。将开发用于过滤基于药效团和分子形状特性的化合物库的算法。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，而不是与化合物数据库的大小相比，可以对数十亿化学结构进行可扩展和快速的过滤。还将开发出利用调制解调器图形处理单元的计算能力的过滤重新释放的有效方法。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表上筛选数十亿个化学结构。工具的互动性能将支持快速的假设测试和实验，用户将能够提交自己的复合库进行筛查，从而鼓励跨学科协作。相关性（请参阅说明）：拟议的研究将导致新颖的算法和系统，用于储存，检索和分析化学数据，以支持对治疗兴趣化合物的快速鉴定。这些算法的成功应用将减少新药开发的成本和时间。