2018 ComparisonofMaximumCommonSubgra

• (Duesbury et al., 2018) ⇒ Edmund Duesbury, John Holliday, and Peter Willett. (2018). “Comparison of Maximum Common Subgraph Isomorphism Algorithms for the Alignment of 2D Chemical Structures.” In: ChemMedChem Journal, 13(6). doi:10.1002/cmdc.201700482, PMID: 29057611 .

Subject Headings: Maximum Common Subgraph Algorithm

Notes

• e-Print: https://onlinelibrary.wiley.com/doi/full/10.1002/cmdc.201700482

Cited By

• http://scholar.google.com/scholar?q=%222018%22+Comparison+of+Maximum+Common+Subgraph+Isomorphism+Algorithms+for+the+Alignment+of+2D+Chemical+Structures

Quotes

Author Keywords

• Chemoinformatics; Drug Discovery; Maximum Common Subgraph; Maximum Common Substructure; Molecular Alignment.

Abstract

The identification of the largest substructure in common when two (or more) molecules are overlaid is important for several applications in chemoinformatics, and can be implemented using a maximum common subgraph (MCS) algorithm. Many such algorithms have been reported, and it is important to know which are likely to be the useful in operation. A detailed comparison was hence conducted of the efficiency (in terms of CPU time) and the effectiveness (in terms of the size of the MCS identified) of eleven MCS algorithms, some of which were exact and some of which were approximate in character. The algorithms were used to identify both connected and disconnected MCSs on a range of pairs of molecules. The fastest exact algorithms for the connected and disconnected problems were found to be the fMCS and MaxCliqueSeq algorithms, respectively, while the ChemAxon_MCS algorithm was the fastest approximate algorithm for both types of problem.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2018 ComparisonofMaximumCommonSubgra	Edmund Duesbury John Holliday Peter Willett			Comparison of Maximum Common Subgraph Isomorphism Algorithms for the Alignment of 2D Chemical Structures				10.1002/cmdc.201700482