Application of kernel functions for accurate similarity search in large chemical databases

BMC Bioinformatics

Proceedings

Gerald H Lushington¹  Aaron Smalter²  Xiaohong Wang²  Jun Huan² 

[1] Molecular Graphics and Modeling Laboratory, University of Kansas, 66045, Lawrence, Kansas, USA;School of Electrical Engineering and Computer Science University of Kansas, 66045, Lawrence, Kansas, USA;

关键词: Query Processing;    Similarity Search;    Hash Table;    Query Graph;    Chemical Database;   

DOI  :  10.1186/1471-2105-11-S3-S8

来源: Springer

【 摘 要 】

BackgroundSimilaritysearch in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases.ResultsTo bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep.ConclusionsEfficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

【 授权许可】

CC BY   
© Huan et al; licensee BioMed Central Ltd. 2010