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。实验筛选候选药物的化合物文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关系。一百万种化合物的虚拟屏幕可能需要几天的时间，并且需要在计算基础设施上进行大量投资。缺乏可扩展的虚拟