title: CHEMICAL COMPOUND CLASSIFICATION WITH AUTOMATICALLY MINED STRUCTURE PATTERNS.
abstract: In this paper we propose new methods of chemical structure classification based on the integration of graph database mining from data mining and graph kernel functions from machine learning. In our method, we first identify a set of general graph patterns in chemical structure data. These patterns are then used to augment a graph kernel function that calculates the pairwise similarity between molecules. The obtained similarity matrix is used as input to classify chemical compounds via a kernel machines such as the support vector machine (SVM). Our results indicate that the use of a pattern-based approach to graph similarity yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art approaches. In addition, the identification of highly discriminative patterns for activity classification provides evidence that our methods can make generalizations about a compound's function given its chemical structure. While we evaluated our methods on molecular structures, these methods are designed to operate on general graph data and hence could easily be applied to other domains in bioinformatics.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA.
authors.forename: A M
authors.initials: AM
authors.lastname: Smalter
authors.forename: J
authors.initials: J
authors.lastname: Huan
authors.forename: G H
authors.initials: GH
authors.lastname: Lushington
references.citation: J Med Chem. 2005 Apr 7;48(7):2687-94
references.pmid: 15801859
references.citation: Neural Netw. 2005 Oct;18(8):1093-110
references.pmid: 16157471
references.citation: J Med Chem. 1996 Aug 2;39(16):3049-59
references.pmid: 8759626
references.citation: J Chem Inf Comput Sci. 1998 Jul-Aug;38(4):726-35
references.pmid: 9691477
references.citation: Bioinformatics. 2003 Jul 1;19(10):1183-93
references.pmid: 12835260
references.citation: J Chem Inf Model. 2005 May-Jun;45(3):549-61
references.pmid: 15921445