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Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure
[Image: see text] Extended reduced graphs provide summary representations of chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Commonly used similarity measures using reduced graphs convert these graphs into 2D vectors like fingerprints, befo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668628/ https://www.ncbi.nlm.nih.gov/pubmed/30920214 http://dx.doi.org/10.1021/acs.jcim.8b00820 |
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author | Garcia-Hernandez, Carlos Fernández, Alberto Serratosa, Francesc |
author_facet | Garcia-Hernandez, Carlos Fernández, Alberto Serratosa, Francesc |
author_sort | Garcia-Hernandez, Carlos |
collection | PubMed |
description | [Image: see text] Extended reduced graphs provide summary representations of chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Commonly used similarity measures using reduced graphs convert these graphs into 2D vectors like fingerprints, before chemical comparisons are made. This study investigates the effectiveness of a graph-only driven molecular comparison by using extended reduced graphs along with graph edit distance methods for molecular similarity calculation as a tool for ligand-based virtual screening applications, which estimate the bioactivity of a chemical on the basis of the bioactivity of similar compounds. The results proved to be very stable and the graph editing distance method performed better than other methods previously used on reduced graphs. This is exemplified with six publicly available data sets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were also used. In the experiments, our method performed better than other molecular similarity methods which use array representations in most cases. Overall, it is shown that extended reduced graphs along with graph edit distance is a combination of methods that has numerous applications and can identify bioactivity similarities in a structurally diverse group of molecules. |
format | Online Article Text |
id | pubmed-6668628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-66686282020-03-28 Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure Garcia-Hernandez, Carlos Fernández, Alberto Serratosa, Francesc J Chem Inf Model [Image: see text] Extended reduced graphs provide summary representations of chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Commonly used similarity measures using reduced graphs convert these graphs into 2D vectors like fingerprints, before chemical comparisons are made. This study investigates the effectiveness of a graph-only driven molecular comparison by using extended reduced graphs along with graph edit distance methods for molecular similarity calculation as a tool for ligand-based virtual screening applications, which estimate the bioactivity of a chemical on the basis of the bioactivity of similar compounds. The results proved to be very stable and the graph editing distance method performed better than other methods previously used on reduced graphs. This is exemplified with six publicly available data sets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were also used. In the experiments, our method performed better than other molecular similarity methods which use array representations in most cases. Overall, it is shown that extended reduced graphs along with graph edit distance is a combination of methods that has numerous applications and can identify bioactivity similarities in a structurally diverse group of molecules. American Chemical Society 2019-03-28 2019-04-22 /pmc/articles/PMC6668628/ /pubmed/30920214 http://dx.doi.org/10.1021/acs.jcim.8b00820 Text en Copyright © 2019 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes. |
spellingShingle | Garcia-Hernandez, Carlos Fernández, Alberto Serratosa, Francesc Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure |
title | Ligand-Based Virtual Screening Using Graph Edit Distance
as Molecular Similarity Measure |
title_full | Ligand-Based Virtual Screening Using Graph Edit Distance
as Molecular Similarity Measure |
title_fullStr | Ligand-Based Virtual Screening Using Graph Edit Distance
as Molecular Similarity Measure |
title_full_unstemmed | Ligand-Based Virtual Screening Using Graph Edit Distance
as Molecular Similarity Measure |
title_short | Ligand-Based Virtual Screening Using Graph Edit Distance
as Molecular Similarity Measure |
title_sort | ligand-based virtual screening using graph edit distance
as molecular similarity measure |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668628/ https://www.ncbi.nlm.nih.gov/pubmed/30920214 http://dx.doi.org/10.1021/acs.jcim.8b00820 |
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