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

基本信息

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
项目类别：
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

项目摘要

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）：化学结构的产生，操纵，储存和检索以及随后的各种特性的计算，通常与其生物学活动有关，对药物发现非常重要。近年来，由此产生的化学信息学领域已经开花，并且是将数据挖掘和数据库原理应用于化合物集合的温床。这些技术的广泛采用导致了代表化学结构，基于相似性化合物的相似性检索，多样性分析和亚基挖掘的方法。化合物作为图的表示以自然的方式捕获化学结构的基本方面，可以轻松传达。最新的图形查询和采矿技术表现出了可伸缩性的巨大希望，以及与传统表示技术（例如指纹）相比，结果的质量提高了。这些技术包括新颖的图形匹配方式，层次索引结构中的图形组织以及一组图表以找到统计上代表性过多的基序。拟议的研究将基于这些思想开发计算工具，并研究各种和大型数据集的技术的可行性。将基于基于化合物的多样性分析和下部结构挖掘的基于图的技术与基于化学结构的其他表示的竞争技术进行比较。最后，将开发将化合物数据库与生物数据库集成的系统。预计所得的分析方法将对复杂，耗时和昂贵的药物发现过程产生重大影响。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。开发的系统还将将有关化合物的信息与生物活性和蛋白质相互作用网络相结合，从而实现更有效的药物发现。