Literature DB >> 11911700

Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm.

John W Raymond1, Eleanor J Gardiner, Peter Willett.   

Abstract

Recently a method (RASCAL) for determining graph similarity using a maximum common edge subgraph algorithm has been proposed which has proven to be very efficient when used to calculate the relative similarity of chemical structures represented as graphs. This paper describes heuristics which simplify a RASCAL similarity calculation by taking advantage of certain properties specific to chemical graph representations of molecular structure. These heuristics are shown experimentally to increase the efficiency of the algorithm, especially at more distant values of chemical graph similarity.

Year:  2002        PMID: 11911700     DOI: 10.1021/ci010381f

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  19 in total

1.  Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases.

Authors:  John W Raymond; Peter Willett
Journal:  J Comput Aided Mol Des       Date:  2002-01       Impact factor: 3.686

2.  Maximum common subgraph isomorphism algorithms for the matching of chemical structures.

Authors:  John W Raymond; Peter Willett
Journal:  J Comput Aided Mol Des       Date:  2002-07       Impact factor: 3.686

3.  Use of graph theory to identify patterns of deprivation and high morbidity and mortality in public health data sets.

Authors:  Peter A Bath; Cheryl Craigs; Ravi Maheswaran; John Raymond; Peter Willett
Journal:  J Am Med Inform Assoc       Date:  2005-07-27       Impact factor: 4.497

Review 4.  Cheminformatics analysis and learning in a data pipelining environment.

Authors:  Moises Hassan; Robert D Brown; Shikha Varma-O'brien; David Rogers
Journal:  Mol Divers       Date:  2006-09-22       Impact factor: 2.943

5.  Application of kernel functions for accurate similarity search in large chemical databases.

Authors:  Xiaohong Wang; Jun Huan; Aaron Smalter; Gerald H Lushington
Journal:  BMC Bioinformatics       Date:  2010-04-29       Impact factor: 3.169

6.  Small Molecule Subgraph Detector (SMSD) toolkit.

Authors:  Syed Asad Rahman; Matthew Bashton; Gemma L Holliday; Rainer Schrader; Janet M Thornton
Journal:  J Cheminform       Date:  2009-08-10       Impact factor: 5.514

7.  Semantic similarity for automatic classification of chemical compounds.

Authors:  João D Ferreira; Francisco M Couto
Journal:  PLoS Comput Biol       Date:  2010-09-23       Impact factor: 4.475

8.  ChemmineR: a compound mining framework for R.

Authors:  Yiqun Cao; Anna Charisi; Li-Chang Cheng; Tao Jiang; Thomas Girke
Journal:  Bioinformatics       Date:  2008-07-02       Impact factor: 6.937

9.  A maximum common substructure-based algorithm for searching and predicting drug-like compounds.

Authors:  Yiqun Cao; Tao Jiang; Thomas Girke
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

Review 10.  Genome-scale models of bacterial metabolism: reconstruction and applications.

Authors:  Maxime Durot; Pierre-Yves Bourguignon; Vincent Schachter
Journal:  FEMS Microbiol Rev       Date:  2008-12-03       Impact factor: 16.408

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