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Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model
A priori calculation of thermophysical properties and predictive thermodynamic models can be very helpful for developing new industrial processes. Group contribution methods link the target property to contributions based on chemical groups or other molecular subunits of a given molecule. However, t...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701077/ https://www.ncbi.nlm.nih.gov/pubmed/33430960 http://dx.doi.org/10.1186/s13321-019-0382-3 |
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author | Müller, Simon |
author_facet | Müller, Simon |
author_sort | Müller, Simon |
collection | PubMed |
description | A priori calculation of thermophysical properties and predictive thermodynamic models can be very helpful for developing new industrial processes. Group contribution methods link the target property to contributions based on chemical groups or other molecular subunits of a given molecule. However, the fragmentation of the molecule into its subunits is usually done manually impeding the fast testing and development of new group contribution methods based on large databases of molecules. The aim of this work is to develop strategies to overcome the challenges that arise when attempting to fragment molecules automatically while keeping the definition of the groups as simple as possible. Furthermore, these strategies are implemented in two fragmentation algorithms. The first algorithm finds only one solution while the second algorithm finds all possible fragmentations. Both algorithms are tested to fragment a database of 20,000+ molecules for use with the group contribution model Universal Quasichemical Functional Group Activity Coefficients (UNIFAC). Comparison of the results with a reference database shows that both algorithms are capable of successfully fragmenting all the molecules automatically. Furthermore, when applying them on a larger database it is shown, that the newly developed algorithms are capable of fragmenting structures previously thought not possible to fragment. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-019-0382-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6701077 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-67010772019-08-26 Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model Müller, Simon J Cheminform Research Article A priori calculation of thermophysical properties and predictive thermodynamic models can be very helpful for developing new industrial processes. Group contribution methods link the target property to contributions based on chemical groups or other molecular subunits of a given molecule. However, the fragmentation of the molecule into its subunits is usually done manually impeding the fast testing and development of new group contribution methods based on large databases of molecules. The aim of this work is to develop strategies to overcome the challenges that arise when attempting to fragment molecules automatically while keeping the definition of the groups as simple as possible. Furthermore, these strategies are implemented in two fragmentation algorithms. The first algorithm finds only one solution while the second algorithm finds all possible fragmentations. Both algorithms are tested to fragment a database of 20,000+ molecules for use with the group contribution model Universal Quasichemical Functional Group Activity Coefficients (UNIFAC). Comparison of the results with a reference database shows that both algorithms are capable of successfully fragmenting all the molecules automatically. Furthermore, when applying them on a larger database it is shown, that the newly developed algorithms are capable of fragmenting structures previously thought not possible to fragment. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-019-0382-3) contains supplementary material, which is available to authorized users. Springer International Publishing 2019-08-20 /pmc/articles/PMC6701077/ /pubmed/33430960 http://dx.doi.org/10.1186/s13321-019-0382-3 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Müller, Simon Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model |
title | Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model |
title_full | Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model |
title_fullStr | Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model |
title_full_unstemmed | Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model |
title_short | Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model |
title_sort | flexible heuristic algorithm for automatic molecule fragmentation: application to the unifac group contribution model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701077/ https://www.ncbi.nlm.nih.gov/pubmed/33430960 http://dx.doi.org/10.1186/s13321-019-0382-3 |
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