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An algorithm to classify homologous series within compound datasets

Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domains, including natural products, environmental chemistry, and drug design. However, many hom...

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Autores principales: Lai, Adelene, Schaub, Jonas, Steinbeck, Christoph, Schymanski, Emma L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746203/
https://www.ncbi.nlm.nih.gov/pubmed/36510332
http://dx.doi.org/10.1186/s13321-022-00663-y
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author Lai, Adelene
Schaub, Jonas
Steinbeck, Christoph
Schymanski, Emma L.
author_facet Lai, Adelene
Schaub, Jonas
Steinbeck, Christoph
Schymanski, Emma L.
author_sort Lai, Adelene
collection PubMed
description Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domains, including natural products, environmental chemistry, and drug design. However, many homologous compounds remain unannotated as such in compound datasets, which poses obstacles to understanding chemical diversity and their analytical identification via database matching. To overcome these challenges, an algorithm to detect homologous series within compound datasets was developed and implemented using the RDKit. The algorithm takes a list of molecules as SMILES strings and a monomer (i.e., repeating unit) encoded as SMARTS as its main inputs. In an iterative process, substructure matching of repeating units, molecule fragmentation, and core detection lead to homologous series classification through grouping of identical cores. Three open compound datasets from environmental chemistry (NORMAN Suspect List Exchange, NORMAN-SLE), exposomics (PubChemLite for Exposomics), and natural products (the COlleCtion of Open NatUral producTs, COCONUT) were subject to homologous series classification using the algorithm. Over 2000, 12,000, and 5000 series with CH(2) repeating units were classified in the NORMAN-SLE, PubChemLite, and COCONUT respectively. Validation of classified series was performed using published homologous series and structure categories, including a comparison with a similar existing method for categorising PFAS compounds. The OngLai algorithm and its implementation for classifying homologues are openly available at: https://github.com/adelenelai/onglai-classify-homologues. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00663-y.
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spelling pubmed-97462032022-12-14 An algorithm to classify homologous series within compound datasets Lai, Adelene Schaub, Jonas Steinbeck, Christoph Schymanski, Emma L. J Cheminform Research Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domains, including natural products, environmental chemistry, and drug design. However, many homologous compounds remain unannotated as such in compound datasets, which poses obstacles to understanding chemical diversity and their analytical identification via database matching. To overcome these challenges, an algorithm to detect homologous series within compound datasets was developed and implemented using the RDKit. The algorithm takes a list of molecules as SMILES strings and a monomer (i.e., repeating unit) encoded as SMARTS as its main inputs. In an iterative process, substructure matching of repeating units, molecule fragmentation, and core detection lead to homologous series classification through grouping of identical cores. Three open compound datasets from environmental chemistry (NORMAN Suspect List Exchange, NORMAN-SLE), exposomics (PubChemLite for Exposomics), and natural products (the COlleCtion of Open NatUral producTs, COCONUT) were subject to homologous series classification using the algorithm. Over 2000, 12,000, and 5000 series with CH(2) repeating units were classified in the NORMAN-SLE, PubChemLite, and COCONUT respectively. Validation of classified series was performed using published homologous series and structure categories, including a comparison with a similar existing method for categorising PFAS compounds. The OngLai algorithm and its implementation for classifying homologues are openly available at: https://github.com/adelenelai/onglai-classify-homologues. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00663-y. Springer International Publishing 2022-12-13 /pmc/articles/PMC9746203/ /pubmed/36510332 http://dx.doi.org/10.1186/s13321-022-00663-y 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
Lai, Adelene
Schaub, Jonas
Steinbeck, Christoph
Schymanski, Emma L.
An algorithm to classify homologous series within compound datasets
title An algorithm to classify homologous series within compound datasets
title_full An algorithm to classify homologous series within compound datasets
title_fullStr An algorithm to classify homologous series within compound datasets
title_full_unstemmed An algorithm to classify homologous series within compound datasets
title_short An algorithm to classify homologous series within compound datasets
title_sort algorithm to classify homologous series within compound datasets
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746203/
https://www.ncbi.nlm.nih.gov/pubmed/36510332
http://dx.doi.org/10.1186/s13321-022-00663-y
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