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

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性质的计算（通常与其生物活性相关）对于药物发现已经变得极其重要。由此产生的化学信息学领域近年来蓬勃发展，并已成为数据挖掘和数据库原则应用于化合物集合的温床。这些技术的广泛采用导致了改进的方法，用于表示化学结构，基于相似性的化合物检索，多样性分析和子结构挖掘。化合物的图形表示以自然的方式捕捉化学结构的基本方面，可以很容易地传达。最近的图查询和挖掘技术已经证明了可扩展性的巨大希望，以及比传统的表示技术，如指纹的结果质量的提高。这些技术包括图匹配的新方法，分层索引结构中的图的组织，以及挖掘一组图以找到统计上过度表示的图案。拟议的研究将根据这些想法开发计算工具，并调查这些技术在不同和大型数据集上的可行性。基于图形的技术相似的化合物检索，多样性分析，和子结构挖掘将进行比较，竞争技术的基础上其他表示的化学结构。最后，将开发一个将化学化合物数据库与生物数据库相结合的系统。由此产生的分析方法预计将对药物发现的复杂，耗时和昂贵的过程产生重大影响。化学化合物的基于图形的表示导致化学空间的更准确的实现。在图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。该系统还将整合具有生物活性和蛋白质相互作用网络的化合物信息，从而实现更有效的药物发现。

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.