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Title: Breadth-first search approach to enumeration of tree-like chemical compounds.
Authors: Zhao, Yang
Hayashida, Morihiro  kyouindb  KAKEN_id
Jindalertudomdee, Jira
Nagamochi, Hiroshi  kyouindb  KAKEN_id
Akutsu, Tatsuya  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-9763-797X (unconfirmed)
Author's alias: 林田, 守広
Keywords: Enumeration
chemical graphs
breadth-first search
drug design
tree structure
Issue Date: 15-Nov-2013
Publisher: World Scientific Publishing
Journal title: Journal of bioinformatics and computational biology
Volume: 11
Issue: 6
Thesis number: 1343007
Abstract: Molecular enumeration plays a basic role in the design of drugs, which has been studied by mathematicians, computer scientists, and chemists for quite a long time. Although many researchers are involved in developing enumeration algorithms specific to drug design systems, molecular enumeration is still a hard problem to date due to its exponentially increasing large search space with larger number of atoms. To alleviate this defect, we propose efficient algorithms, BfsSimEnum and BfsMulEnum to enumerate tree-like molecules without and with multiple bonds, respectively, where chemical compounds are represented as molecular graphs. In order to reduce the large search space, we adjust some important concepts such as left-heavy, center-rooted, and normal form to molecular tree graphs. Different from many existing approaches, BfsSimEnum and BfsMulEnum firstly enumerate tree-like compounds by breadth-first search order. Computational experiments are performed to compare with several existing methods. The results suggest that our proposed methods are exact and more efficient.
Rights: Electronic version of an article published as Journal of Bioinformatics and Computational Biology, 11 (2013), 1343007, DOI: 10.1142/S0219720013430075 © World Scientific Publishing Company http://www.worldscientific.com/worldscinet/jbcb
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/196770
DOI(Published Version): 10.1142/S0219720013430075
PubMed ID: 24372036
Appears in Collections:Journal Articles

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