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.
On the unification of the graph edit distance and graph matching problems
