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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Research Article

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

Author(s): Carlos Garcia-Hernandez, Alberto Fernández* and Francesc Serratosa

Volume 20, Issue 18, 2020

Page: [1582 - 1592] Pages: 11

DOI: 10.2174/1568026620666200603122000

open access plus

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.

Keywords: Structure-activity relationships, Graph edit distance, Extended reduced graph, Virtual screening, Molecular similarity, Machine learning.

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