Heuristics for Similarity Searching of Chemical Graphs Using a Maximum Common Edge Subgraph Algorithm

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.

Chemoinformatics at the University of Sheffield 2002–2014

“…[1][2][3][4] The work of the chemoinformatics research group in Sheffield has always had a strong algorithmic and methodological focus, this reflecting our location in an informatics, rather than a chemical, academic department. We have thus drawn extensively on computational techniques from, e.g., graph theory, [5][6] cluster analysis, [7][8] image processing [9][10] and combinatorial optimisation [11][12] inter alia to design and implement a wide range of chemoinformatics applications. Lynch and Willett [1] and Bishop et al [4] have described Sheffield work in chemoinformatics for the periods 1965-1985 and 1986-2002, respectively.…”

Finding Frequent Patterns in a Large Sparse Graph*

“…Frequent patterns play a critical role in many data mining tasks as they can be used among other to derive association rules [1], act as composite features for classification algorithms [14,56,63,51,22,50,15], cluster the (graph) transactions [1,48,35,36,49,24], and help in determining the similarity between graphs [54,23,42,59,9,49,13,60,66]. Within the context of graphs, the most widely used definition of a pattern is that of a connected subgraph [8,68,32,29,69,30,44] and is the definition that we will use in this paper.…”

Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures

“…This provides a natural way of calculating the degree of similarity between a pair of molecules but the NP-complete nature of the maximum common subgraph isomorphism problem has ruled out the large-scale use of MCS-based similarities. We have recently described a new MCS algorithm, called RASCAL, that is sufficiently rapid in execution to permit graph-based similarity searching of large chemical databases 16,17 and that seems to provide a viable complement, or even an alternative, to existing, fingerprint-based approaches to virtual screening 18 .…”