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Application of deep metric learning to molecular graph similarity
Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular graph similarity based on distance between l...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917631/ https://www.ncbi.nlm.nih.gov/pubmed/35279188 http://dx.doi.org/10.1186/s13321-022-00595-7 |
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author | Coupry, Damien E. Pogány, Peter |
author_facet | Coupry, Damien E. Pogány, Peter |
author_sort | Coupry, Damien E. |
collection | PubMed |
description | Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular graph similarity based on distance between learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. Finally, we compare the applications of the embedding to standard practices, with a focus on known failure points and edge cases; concluding that our approach can be used in conjunction to existing methods. |
format | Online Article Text |
id | pubmed-8917631 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-89176312022-03-21 Application of deep metric learning to molecular graph similarity Coupry, Damien E. Pogány, Peter J Cheminform Research Article Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular graph similarity based on distance between learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. Finally, we compare the applications of the embedding to standard practices, with a focus on known failure points and edge cases; concluding that our approach can be used in conjunction to existing methods. Springer International Publishing 2022-03-12 /pmc/articles/PMC8917631/ /pubmed/35279188 http://dx.doi.org/10.1186/s13321-022-00595-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Coupry, Damien E. Pogány, Peter Application of deep metric learning to molecular graph similarity |
title | Application of deep metric learning to molecular graph similarity |
title_full | Application of deep metric learning to molecular graph similarity |
title_fullStr | Application of deep metric learning to molecular graph similarity |
title_full_unstemmed | Application of deep metric learning to molecular graph similarity |
title_short | Application of deep metric learning to molecular graph similarity |
title_sort | application of deep metric learning to molecular graph similarity |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917631/ https://www.ncbi.nlm.nih.gov/pubmed/35279188 http://dx.doi.org/10.1186/s13321-022-00595-7 |
work_keys_str_mv | AT couprydamiene applicationofdeepmetriclearningtomoleculargraphsimilarity AT poganypeter applicationofdeepmetriclearningtomoleculargraphsimilarity |