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

Authors
  • Garcia-Hernandez, Carlos
  • Fernández, Alberto
  • Serratosa, Francesc
Type
Published Article
Journal
Current Topics in Medicinal Chemistry
Publisher
Bentham Science Publishers
Publication Date
Jul 01, 2020
Volume
20
Issue
18
Pages
1582–1592
Identifiers
DOI: 10.2174/1568026620666200603122000
PMID: 32493194
PMCID: PMC7536799
Source
PubMed Central
Keywords
License
Green

Abstract

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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