Detection of the Largest Patterns Common to a Set of Molecules, Using 2D Graphs and 3D Skeletons

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“…In their respective papers, Levi suggested using a clique detection procedure due to Grasselli [37], and Barrow and Burstall proposed using an algorithm due to Bron and Kerbosch [38]. Until recently [28], the BronKerbosch algorithm was the clique detection procedure of choice for clique-based MCIS applications [1,24,[39][40][41][42][43][44].…”

“…In related work, Bayada and Johnson [44] add a post-processing step to the algorithm to allow some measure of disconnectedness in the MCES solution. This algorithm was experimentally studied by Brown [70], who found that it frequently resulted in MCES approximations differing from the actual MCES; this led Brown to the implementation of the GA approach described in [57].…”

“…Most of the algorithms described in this paper have been presented from the perspective of arbitrary or 2D graphs, and much of the published literature on the 3D MCS problem simply adapts these algorithms to the 3D case [1,[41][42][43][44]77]. The Barakat-Dean algorithm is an example of an algorithm that was originally proposed in a 3D formulation, and so is the algorithm described by Crandell and Smith [78], although even this owes much to the algorithm of Varkony et al [66], in that it attempts to "grow" a 3D MCIS iteratively.…”