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: 7293378
• 负责人: William Maxwell Lindstrom
• 金额: $ 22.33万
• 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 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7293378
• 关键词: Adopted; Adoption; Aging; Algorithms; Area; Back; Biological; Biological databases; Bite; Cations; Chemical Structure; Chemicals; Closure; Collection; Complement; Complex; Computer software; Count; Data; Data Analyses; Data Set; Databases; Descriptor; Development; Docking; Drug Design; Effectiveness; Elements; Excretory function; Facility Construction Funding Category; Feasibility Studies; Figs - dietary; Fingerprint; Flowers; Generations; Goals; Graph; Growth; Imagery; Informatics; Information Resources Management; Information Retrieval; Isomerism; Lead; Learning; Ligands; Measures; Metabolism; Methods; Metric; Mining; Modeling; Molecular; Molecular Bank; Molecular Models; Molecular Structure; Nitrogen; Numbers; Object Attachment; Pathway interactions; Pattern; Pharmacologic Substance; Phase; Play; Positioning Attribute; Principal Investigator; Printing; Probability; Process; Property; Proteins; Range; Research; Retrieval; Role; Scheme; Scientist; Score; Screening procedure; Shapes; Small Business Funding Mechanisms; Small Business Innovation Research Grant; Structure; Surface; System; Techniques; Therapeutic; Time; Toxic effect; Translating; Translations; Trees; Work; absorption; abstracting; base; cheminformatics; combinatorial chemistry; computerized tools; data mining; data space; design; drug discovery; functional group; high throughput screening; improved; indexing; man; novel; programs; repository; research and development; research study; size; small molecule; small molecule libraries; tool; vector; virtual

DESCRIPTION (provided by applicant): The generation, manipulation, storage and retrieval of chemical structures and subsequent calculation of various properties, often related to their biological activity, have become extremely important for drug discovery. The resulting field of Cheminformatics has blossomed in recent years and has been a hotbed for the application of data mining and database principles to collections of chemical compounds. The wide adoption of these techniques has led to im- proved methods for representation of chemical structures, similarity-based retrieval of chemical compounds, diversity analysis, and substructure mining. The representation of chemical compounds as graphs captures the essential aspects of chemical structures in a natural way that can be communicated easily. Recent techniques for graph querying and mining have demonstrated great promise for scalability as well as an improved quality of results over traditional representation techniques such as fingerprints. These techniques include novel ways of graph matching, the organization of graphs in a hierarchical index structure, and the mining of a set of graphs to find statistically over-represented motifs. The proposed research will develop computational tools based on these ideas and investigate the feasibility of the techniques on diverse and large data sets. Graph-based techniques for similar compound retrieval, diversity analysis, and substructure mining will be compared to competing techniques based on other representations of chemical structures. Finally, a system that integrates chemical compound databases with biological databases will be developed. The resulting analysis methods are expected to make a significant impact on the complex, time-consuming, and expensive process of drug discovery. 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 also integrate information on chemical compounds with biological activity and protein interaction networks, thus enabling more efficient drug discovery.

描述（由申请人提供）：化学结构的生成、操作、存储和检索以及随后的各种特性（通常与其生物活性相关）的计算对于药物发现变得极其重要。由此产生的化学信息学领域近年来蓬勃发展，并成为将数据挖掘和数据库原理应用于化合物收集的温床。这些技术的广泛采用改进了化学结构表示、基于相似性的化合物检索、多样性分析和子结构挖掘的方法。化合物以图表的形式表示，以一种易于交流的自然方式捕获了化学结构的基本方面。最近的图查询和挖掘技术已经证明了可扩展性的巨大前景，并且比指纹等传统表示技术提高了结果质量。这些技术包括图形匹配的新颖方法、分层索引结构中的图形组织以及挖掘一组图形以查找统计上过度代表性的主题。拟议的研究将开发基于这些想法的计算工具，并研究该技术在多样化和大型数据集上的可行性。用于相似化合物检索、多样性分析和子结构挖掘的基于图的技术将与基于其他化学结构表示的竞争技术进行比较。最后，将开发一个将化学化合物数据库与生物数据库集成的系统。由此产生的分析方法预计将对复杂、耗时且昂贵的药物发现过程产生重大影响。基于图形的化合物表示可以更准确地实现化学空间。在图形查询和挖掘中使用最新技术将使数据分析能够扩展到数百万种化合物。开发的系统还将整合化学化合物的信息与生物活性和蛋白质相互作用网络，从而实现更有效的药物发现。