Literature DB >> 22889967

Graph mining: procedure, application to drug discovery and recent advances.

Ichigaku Takigawa1, Hiroshi Mamitsuka.   

Abstract

Combinatorial chemistry has generated chemical libraries and databases with a huge number of chemical compounds, which include prospective drugs. Chemical structures of compounds can be molecular graphs, to which a variety of graph-based techniques in computer science, specifically graph mining, can be applied. The most basic way for analyzing molecular graphs is using structural fragments, so-called subgraphs in graph theory. The mainstream technique in graph mining is frequent subgraph mining, by which we can retrieve essential subgraphs in given molecular graphs. In this article we explain the idea and procedure of mining frequent subgraphs from given molecular graphs, raising some real applications, and we describe the recent advances of graph mining.
Copyright © 2012. Published by Elsevier Ltd.

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Year:  2012        PMID: 22889967     DOI: 10.1016/j.drudis.2012.07.016

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  7 in total

1.  A chemo-centric view of human health and disease.

Authors:  Miquel Duran-Frigola; David Rossell; Patrick Aloy
Journal:  Nat Commun       Date:  2014-12-01       Impact factor: 14.919

2.  A low dimensional approach on network characterization.

Authors:  Benjamin Y S Li; Choujun Zhan; Lam F Yeung; King T Ko; Genke Yang
Journal:  PLoS One       Date:  2014-10-16       Impact factor: 3.240

3.  pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures.

Authors:  Douglas E V Pires; Tom L Blundell; David B Ascher
Journal:  J Med Chem       Date:  2015-04-22       Impact factor: 7.446

4.  Ensemble learning method for the prediction of new bioactive molecules.

Authors:  Lateefat Temitope Afolabi; Faisal Saeed; Haslinda Hashim; Olutomilayo Olayemi Petinrin
Journal:  PLoS One       Date:  2018-01-12       Impact factor: 3.240

5.  graphkernels: R and Python packages for graph comparison.

Authors:  Mahito Sugiyama; M Elisabetta Ghisu; Felipe Llinares-López; Karsten Borgwardt
Journal:  Bioinformatics       Date:  2018-02-01       Impact factor: 6.937

6.  A de novo substructure generation algorithm for identifying the privileged chemical fragments of liver X receptorβ agonists.

Authors:  He Peng; Zhihong Liu; Xin Yan; Jian Ren; Jun Xu
Journal:  Sci Rep       Date:  2017-09-11       Impact factor: 4.379

Review 7.  Grasping frequent subgraph mining for bioinformatics applications.

Authors:  Aida Mrzic; Pieter Meysman; Wout Bittremieux; Pieter Moris; Boris Cule; Bart Goethals; Kris Laukens
Journal:  BioData Min       Date:  2018-09-03       Impact factor: 2.522

  7 in total

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