Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Latendresse, Mario, Malerich, Jeremiah P., Herson, James, Krummenacker, Markus, Szeto, Judy, Vu, Vi-Anh, Collins, Nathan, Madrid, Peter B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
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
_version_ 1785105519973761024
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
work_keys_str_mv AT latendressemario synroutearetrosyntheticplanningsoftware
AT malerichjeremiahp synroutearetrosyntheticplanningsoftware
AT hersonjames synroutearetrosyntheticplanningsoftware
AT krummenackermarkus synroutearetrosyntheticplanningsoftware
AT szetojudy synroutearetrosyntheticplanningsoftware
AT vuvianh synroutearetrosyntheticplanningsoftware
AT collinsnathan synroutearetrosyntheticplanningsoftware
AT madridpeterb synroutearetrosyntheticplanningsoftware