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SynRoute: A Retrosynthetic Planning Software
[Image: see text] Computer-assisted synthetic planning has seen major advancements that stem from the availability of large reaction databases and artificial intelligence methodologies. SynRoute is a new retrosynthetic planning software tool that uses a relatively small number of general reaction te...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498441/ https://www.ncbi.nlm.nih.gov/pubmed/37635298 http://dx.doi.org/10.1021/acs.jcim.3c00491 |
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author | Latendresse, Mario Malerich, Jeremiah P. Herson, James Krummenacker, Markus Szeto, Judy Vu, Vi-Anh Collins, Nathan Madrid, Peter B. |
author_facet | Latendresse, Mario Malerich, Jeremiah P. Herson, James Krummenacker, Markus Szeto, Judy Vu, Vi-Anh Collins, Nathan Madrid, Peter B. |
author_sort | Latendresse, Mario |
collection | PubMed |
description | [Image: see text] Computer-assisted synthetic planning has seen major advancements that stem from the availability of large reaction databases and artificial intelligence methodologies. SynRoute is a new retrosynthetic planning software tool that uses a relatively small number of general reaction templates, currently 263, along with a literature-based reaction database to find short, practical synthetic routes for target compounds. For each reaction template, a machine learning classifier is trained using data from the Pistachio reaction database to predict whether new computer-generated reactions based on the template are likely to work experimentally in the laboratory. This reaction generation methodology is used together with a vectorized Dijkstra-like search of top-scoring routes organized by synthetic strategies for easy browsing by a synthetic chemist. SynRoute was able to find routes for an average of 83% of compounds based on selection of random subsets of drug-like compounds from the ChEMBL database. Laboratory evaluation of 12 routes produced by SynRoute, to synthesize compounds not from the previous random subsets, demonstrated the ability to produce feasible overall synthetic strategies for all compounds evaluated. |
format | Online Article Text |
id | pubmed-10498441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-104984412023-09-14 SynRoute: A Retrosynthetic Planning Software Latendresse, Mario Malerich, Jeremiah P. Herson, James Krummenacker, Markus Szeto, Judy Vu, Vi-Anh Collins, Nathan Madrid, Peter B. J Chem Inf Model [Image: see text] Computer-assisted synthetic planning has seen major advancements that stem from the availability of large reaction databases and artificial intelligence methodologies. SynRoute is a new retrosynthetic planning software tool that uses a relatively small number of general reaction templates, currently 263, along with a literature-based reaction database to find short, practical synthetic routes for target compounds. For each reaction template, a machine learning classifier is trained using data from the Pistachio reaction database to predict whether new computer-generated reactions based on the template are likely to work experimentally in the laboratory. This reaction generation methodology is used together with a vectorized Dijkstra-like search of top-scoring routes organized by synthetic strategies for easy browsing by a synthetic chemist. SynRoute was able to find routes for an average of 83% of compounds based on selection of random subsets of drug-like compounds from the ChEMBL database. Laboratory evaluation of 12 routes produced by SynRoute, to synthesize compounds not from the previous random subsets, demonstrated the ability to produce feasible overall synthetic strategies for all compounds evaluated. American Chemical Society 2023-08-28 /pmc/articles/PMC10498441/ /pubmed/37635298 http://dx.doi.org/10.1021/acs.jcim.3c00491 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Latendresse, Mario Malerich, Jeremiah P. Herson, James Krummenacker, Markus Szeto, Judy Vu, Vi-Anh Collins, Nathan Madrid, Peter B. SynRoute: A Retrosynthetic Planning Software |
title | SynRoute: A Retrosynthetic
Planning Software |
title_full | SynRoute: A Retrosynthetic
Planning Software |
title_fullStr | SynRoute: A Retrosynthetic
Planning Software |
title_full_unstemmed | SynRoute: A Retrosynthetic
Planning Software |
title_short | SynRoute: A Retrosynthetic
Planning Software |
title_sort | synroute: a retrosynthetic
planning software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498441/ https://www.ncbi.nlm.nih.gov/pubmed/37635298 http://dx.doi.org/10.1021/acs.jcim.3c00491 |
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