Enumerating tree-like chemical graphs with given upper and lower bounds on path frequencies

BMC Bioinformatics

Proceedings

Tatsuya Akutsu¹  Hiroshi Nagamochi²  Masaaki Shimizu² 

[1] Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, 611-0011, Uji, Kyoto, Japan;Graduate School of Informatics, Kyoto University, 606-8501, Yoshida, Kyoto, Japan;

关键词: Feature Vector;    Multiple Edge;    Canonical Representation;    Enumeration Algorithm;    Label Sequence;   

DOI  :  10.1186/1471-2105-12-S14-S3

来源: Springer

【 摘 要 】

BackgroundEnumeration of chemical graphs satisfying given constraints is one of the fundamental problems in chemoinformatics and bioinformatics since it leads to a variety of useful applications including structure determination of novel chemical compounds and drug design.ResultsIn this paper, we consider the problem of enumerating all tree-like chemical graphs from a given set of feature vectors, which is specified by a pair of upper and lower feature vectors, where a feature vector represents the frequency of prescribed paths in a chemical compound to be constructed. This problem can be solved by applying the algorithm proposed by Ishida et al. to each single feature vector in the given set, but this method may take much computation time because in general there are many feature vectors in a given set. We propose a new exact branch-and-bound algorithm for the problem so that all the feature vectors in a given set are handled directly. Since we cannot use the bounding operation proposed by Ishida et al. due to upper and lower constraints, we introduce new bounding operations based on upper and lower feature vectors, a bond constraint, and a detachment condition.ConclusionsOur proposed algorithm is useful for enumerating tree-like chemical graphs with given upper and lower bounds on path frequencies.

【 授权许可】

Unknown   
© Shimizu et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.