Scalable tools for the analysis of chemical compounds using graph-based querying

使用基于图形的查询分析化合物的可扩展工具

• 批准号: 7539247
• 负责人: William Maxwell Lindstrom
• 金额: $ 51.9万
• 依托单位: ACELOT, INC.
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7539247
• 关键词: 3-Dimensional; Academia; Address; Adopted; Algorithms; Area; Arts; Avian Influenza; Back; Biochemical; Bioinformatics; Biological; Biological databases; Case Study; Cations; Cells; Cellular biology; Chemical Structure; Chemicals; Chemistry; Clinical Data; Closure; Collection; Complex; Computational Technique; Crystallography; Data; Data Analyses; Data Set; Databases; Descriptor; Development; Diagnostic; Discipline; Docking; Documentation; Drug Industry; Effectiveness; Elements; Excretory function; Face; Facility Construction Funding Category; Figs - dietary; Fingers; Floods; Genome; Glycobiology; Goals; Graph; Growth; HIV-1 protease; Hand; Health; Healthcare; Human; Hydrogen Bonding; Informatics; Information Retrieval; Interdisciplinary Study; Isomerism; Knowledge; Language; Lead; Maps; Marketing; Measures; Metabolic; Metabolism; Methodology; Methods; Mining; Modeling; Molecular; Molecular Bank; Molecular Models; Molecular Structure; Nature; Neuraminidase; Numbers; Outcome; Outcomes Research; Pathway interactions; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Positioning Attribute; Printing; Probability; Problem Solving; Process; Property; Protein p53; Proteins; Public Health; Purpose; Quaternary Protein Structure; Range; Rate; Recording of previous events; Registries; Research; Research Infrastructure; Research Personnel; Retrieval; Scheme; Scientific Advances and Accomplishments; Screening procedure; Signal Transduction; Statistically Significant; Structural Biologist; Structure; Study Section; Surface; System; Systems Biology; Techniques; Technology; Time; Toxic effect; Translating; Trees; Triad Acrylic Resin; Work; absorption; base; cheminformatics; commercialization; computerized tools; concept; design; drug development; drug discovery; functional group; high throughput screening; improved; indexing; innovation; insight; interest; macromolecule; molecular dynamics; novel; novel therapeutics; rapid growth; research and development; research study; size; small molecule; software development; structural biology; tool; trend; virtual

DESCRIPTION (provided by applicant): Our current capacity to generate chemical and structural biological data far exceeds our capability to meaningfully assimilate it. The data describes molecules and biological macromolecules and associated properties. A principle common to the structure of all chemical and biological macromolecular entities is the composition of objects related by energetic interaction. A natural representation of all such entities is a graph composed of nodes related by edges. We have developed powerful, scalable techniques that operate on graph databases for efficient similarity searching (Closure-tree), identification of statistically significant subgraphs (GraphRank), and query specification (GraphQL). These techniques are naturally applied to chemical and structural biological data, which are naturally represented as graphs. We have demonstrated the validity of the approach in prior work, and the feasibility in our phase 1 research. The overall goal of this project is to deliver powerful innovative problem solving tools to medicinal chemists, structural biologists, and drug discovery researchers synthesizing ever increasing amounts of chemical, biochemical, structural biological, cell biological, and clinical data. Phase 1 of this project is ongoing and highly successful. We have successfully demonstrated that the Closure- tree and GraphRank algorithms are effective on chemical compound databases of realistic, industrial size. We have developed methods to exploit our knowledge of the nature of chemical databases. Using these methods we have improved similarity query performance time by over an order of magnitude. We have identified several specific aims to purse in Phase 2 of our research. We have rapidly established a professional software development and research infrastructure and developed the tools necessary to support progress toward the goal of solving important problems hindering medicinal chemists and structural biologists conducting modern drug discovery research for the development of new therapeutics. We will pursue four specific aims in our Phase 2 research. (1) We will develop specific additional functionality for Closure-tree and GraphRank, and integrate GraphQL into our chemical and structural bioinformatics tool set. The results of this aim will be used to (2) develop methods and functionality to represent chemical, structural biology, systems biology, and glycobiology data as graphs. Building on these results, we will (3) apply our tool set to specific relevant research problems such as HIV-1 Protease inhibition, Avian Flu neuraminidase inhibition, and p53-protein interactions. Finally, we will (4) assemble a state-of-the-art chemical and structural biological informatics tool set with detailed documentation and relevant case studies. The outcome of this research will be powerful, innovative new tools in the hands of medicinal chemists, structural biologists, and modern drug discovery researchers in academia and the pharmaceutical industry. The tools address significant obstacles in the drug development process and will enable new discoveries and greatly advance the practice of cheminformatic and structural biological data analysis. Through a carefully developed market analysis described in our commercialization plan, we show a growing market for our tools and competitive advantages. Application of our techniques will have significant impact on the interpretation of structural biological data, on pharmaceutical research and modern drug discovery chemistry, and on human health care through the design of new drugs. PUBLIC HEALTH RELEVANCE: Graph-based representation of chemical compounds results in a more accurate realization of the chemical space. The use of recent techniques in graph querying and mining will enable data analysis that can scale to millions of compounds. The developed system will integrate information on chemical compounds with biological activity and protein interaction networks, thus enabling cheaper and faster drug discovery.

描述（由申请人提供）：我们当前生成化学和结构生物学数据的能力远远超过了我们有意义地吸收它的能力。数据描述了分子和生物学大分子和相关特性。所有化学和生物大分子实体的结构共有的原理是通过能量相互作用相关的物体组成。所有此类实体的自然表示是由边缘相关的节点组成的图。我们已经开发了功能强大的可扩展技术，这些技术在图形数据库上运行，以进行有效的相似性搜索（闭合树），识别统计学意义的子图（GraphRank）和查询规范（GraphQL）。这些技术自然应用于化学和结构生物学数据，这些数据自然表示为图。我们已经证明了该方法在先前的工作中的有效性以及我们的第一阶段研究中的可行性。该项目的总体目标是为药物学家，结构生物学家和药物发现研究人员提供强大的创新问题解决工具，从而综合了不断增加的化学，生化，结构生物学，细胞生物学和临床数据。该项目的第一阶段正在进行中，并且非常成功。我们成功证明了闭合树和图形源算法对现实，工业规模的化学化合物数据库有效。我们已经开发了利用化学数据库性质知识的方法。使用这些方法，我们通过超过一个数量级来提高相似性查询性能时间。我们已经确定了在我们研究的第二阶段中的几个具体目标。我们已经迅速建立了专业的软件开发和研究基础设施，并开发了支持进步所必需的工具，以解决阻碍药物化学家和结构性生物学家进行现代药物发现研究的重要问题，以开发新的治疗剂。我们将在第二阶段研究中追求四个具体目标。 （1）我们将为闭合树和GraphRank开发特定的附加功能，并将GraphQl集成到我们的化学和结构生物信息学工具集中。该目标的结果将用于（2）开发代表化学，结构生物学，系统生物学和糖生物学数据的方法和功能。在这些结果的基础上，我们将（3）将工具集应用于特定的相关研究问题，例如HIV-1蛋白酶抑制，禽流感神经氨基酶抑制和p53-蛋白质相互作用。最后，我们（4）组装一个最先进的化学和结构生物学信息学工具，其中包含详细的文档和相关案例研究。这项研究的结果将是强大，创新的新工具，这些工具是药剂师，结构生物学家以及学术界和制药行业的现代药物发现研究人员的手中。这些工具解决了药物开发过程中的重大障碍，并将实现新的发现，并大大推动化学和结构生物学数据分析的实践。通过在我们的商业化计划中描述的精心发达的市场分析，我们为工具和竞争优势展示了不断增长的市场。我们技术的应用将对结构生物学数据，药物研究和现代药物发现化学的解释以及通过新药的设计对人类保健产生重大影响。公共卫生相关性：化学化合物的基于图的表示会导致化学空间更准确地实现。最新技术在图查询和采矿中的使用将实现可以扩展到数百万化合物的数据分析。开发的系统将与生物活性和蛋白质相互作用网络的化合物有关，从而可以更便宜，更快地发现药物。