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CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration
In computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465358/ https://www.ncbi.nlm.nih.gov/pubmed/33431005 http://dx.doi.org/10.1186/s13321-020-00452-5 |
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author | Shibukawa, Ryosuke Ishida, Shoichi Yoshizoe, Kazuki Wasa, Kunihiro Takasu, Kiyosei Okuno, Yasushi Terayama, Kei Tsuda, Koji |
author_facet | Shibukawa, Ryosuke Ishida, Shoichi Yoshizoe, Kazuki Wasa, Kunihiro Takasu, Kiyosei Okuno, Yasushi Terayama, Kei Tsuda, Koji |
author_sort | Shibukawa, Ryosuke |
collection | PubMed |
description | In computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it is likely that the desired one will not be included. To avoid this, an exact algorithm that lists possible synthetic routes within the chemical reaction network is required, alongside a recommendation of synthetic routes that meet specified criteria based on the chemist’s objectives. Herein, we propose a chemical-reaction-network-based synthetic route recommendation framework called “CompRet” with a mathematically guaranteed enumeration algorithm. In a preliminary experiment, CompRet was shown to successfully provide alternative routes for a known antihistaminic drug, cetirizine. CompRet is expected to promote desirable enumeration-based chemical synthesis searches and aid the development of an interactive CASP framework for chemists. |
format | Online Article Text |
id | pubmed-7465358 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-74653582020-09-02 CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration Shibukawa, Ryosuke Ishida, Shoichi Yoshizoe, Kazuki Wasa, Kunihiro Takasu, Kiyosei Okuno, Yasushi Terayama, Kei Tsuda, Koji J Cheminform Preliminary Communication In computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it is likely that the desired one will not be included. To avoid this, an exact algorithm that lists possible synthetic routes within the chemical reaction network is required, alongside a recommendation of synthetic routes that meet specified criteria based on the chemist’s objectives. Herein, we propose a chemical-reaction-network-based synthetic route recommendation framework called “CompRet” with a mathematically guaranteed enumeration algorithm. In a preliminary experiment, CompRet was shown to successfully provide alternative routes for a known antihistaminic drug, cetirizine. CompRet is expected to promote desirable enumeration-based chemical synthesis searches and aid the development of an interactive CASP framework for chemists. Springer International Publishing 2020-09-01 /pmc/articles/PMC7465358/ /pubmed/33431005 http://dx.doi.org/10.1186/s13321-020-00452-5 Text en © The Author(s) 2020 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/. 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 in a credit line to the data. |
spellingShingle | Preliminary Communication Shibukawa, Ryosuke Ishida, Shoichi Yoshizoe, Kazuki Wasa, Kunihiro Takasu, Kiyosei Okuno, Yasushi Terayama, Kei Tsuda, Koji CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration |
title | CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration |
title_full | CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration |
title_fullStr | CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration |
title_full_unstemmed | CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration |
title_short | CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration |
title_sort | compret: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration |
topic | Preliminary Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465358/ https://www.ncbi.nlm.nih.gov/pubmed/33431005 http://dx.doi.org/10.1186/s13321-020-00452-5 |
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