Abstract

Similarity search in chemical structure databases is an important problem with many applications in chemicalgenomics, drug design, and efficient chemical probe screeningamong 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.To bridge graph kernel function and similarity search inchemical 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, namedG-hash, to large chemical databases. Our results show thatthe 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.

Highlights

  • Elucidate the roles of small organic molecules in biological systems, as studied in chemical genomics, is an emergent and challenging task

  • The analysis of chemical genomics data was done mainly within pharmaceutical companies for therapeutics discovery, and it was estimated that only 1% of chemical information was in the public domains [1]

  • Before we proceed to discuss the algorithmic details, we present some general background materials which include the introduction of the concept of graphs and chemical structures as graphs

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Summary

Introduction

Elucidate the roles of small organic molecules in biological systems, as studied in chemical genomics, is an emergent and challenging task. Most 3D structure based approaches compare threedimensional shapes using a range of molecular descriptors [5][6] Such methods provide fast query processing in large chemical databases but relatively poor accuracy since such methods may lost much of the structure information during compressing the three-dimensional shapes. 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. Below we introduce details of the feature extractiion process, the index structure for fast similarity query and the kernel function for similarity measurement. Based on the hash table, we calculate distances between query graph and graphs in the database.

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