Combining Bipartite Graph Matching and Beam Search for Graph Edit Distance Approximation

2014 ◽  
pp. 117-128 ◽  
Author(s):  
Kaspar Riesen ◽  
Andreas Fischer ◽  
Horst Bunke
Keyword(s):  
Bipartite Graph ◽  
Edit Distance ◽  
Graph Matching ◽  
Beam Search ◽  
Graph Edit Distance ◽  
Distance Approximation ◽  
Bipartite Graph Matching

scholarly journals Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening

2020 ◽  
Vol 20 (18) ◽  
pp. 1582-1592 ◽  
Author(s):  
Carlos Garcia-Hernandez ◽  
Alberto Fernández ◽  
Francesc Serratosa
Keyword(s):  
Virtual Screening ◽  
Edit Distance ◽  
Graph Matching ◽  
Optimization Techniques ◽  
Graph Edit Distance ◽  
Structure Activity ◽  
Future Drug ◽  
Minimum Number ◽  
Type Node ◽  
Activity Information

Background: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. Objective: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. Methods: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. Results: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. Conclusion: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

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