Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases

Background: Similaritysearch 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. Results: To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernelbased 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...

• Type: Conference
• Year: 2009
• Where: BIBM
• Authors: Xiaohong Wang, Jun Huan, Aaron M. Smalter, Gerald H. Lushington
• Keywords: BIBM 2009, Bioinformatics, Chemical Databases, Graph Kernel Function, Similarity Search