Cargando…

Algorithm for the Pruning of Synthesis Graphs

[Image: see text] Synthesis route planning is in the core of chemical intelligence that will power the autonomous chemistry platforms. In this task, we rely on algorithms to generate possible synthesis routes with the help of retro- and forward-synthetic approaches. Generated synthesis routes can be...

Descripción completa

Detalles Bibliográficos
Autores principales: Zahoránszky-Kőhalmi, Gergely, Lysov, Nikita, Vorontcov, Ilia, Wang, Jeffrey, Soundararajan, Jeyaraman, Metaxotos, Dimitrios, Mathew, Biju, Sarosh, Rafat, Michael, Samuel G., Godfrey, Alexander G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9093600/
https://www.ncbi.nlm.nih.gov/pubmed/35438992
http://dx.doi.org/10.1021/acs.jcim.1c01202
_version_ 1784705366785785856
author Zahoránszky-Kőhalmi, Gergely
Lysov, Nikita
Vorontcov, Ilia
Wang, Jeffrey
Soundararajan, Jeyaraman
Metaxotos, Dimitrios
Mathew, Biju
Sarosh, Rafat
Michael, Samuel G.
Godfrey, Alexander G.
author_facet Zahoránszky-Kőhalmi, Gergely
Lysov, Nikita
Vorontcov, Ilia
Wang, Jeffrey
Soundararajan, Jeyaraman
Metaxotos, Dimitrios
Mathew, Biju
Sarosh, Rafat
Michael, Samuel G.
Godfrey, Alexander G.
author_sort Zahoránszky-Kőhalmi, Gergely
collection PubMed
description [Image: see text] Synthesis route planning is in the core of chemical intelligence that will power the autonomous chemistry platforms. In this task, we rely on algorithms to generate possible synthesis routes with the help of retro- and forward-synthetic approaches. Generated synthesis routes can be merged into a synthesis graph which represents theoretical pathways to the target molecule. However, it is often required to modify a synthesis graph due to typical constraints. These constraints might include “undesirable substances”, e.g., an intermediate that the chemist does not favor or substances that might be toxic. Consequently, we need to prune the synthesis graph by the elimination of such undesirable substances. Synthesis graphs can be represented as directed (not necessarily acyclic) bipartite graphs, and the pruning of such graphs in the light of a set of undesirable substances has been an open question. In this study, we present the Synthesis Graph Pruning (SGP) algorithm that addresses this question. The input to the SGP algorithm is a synthesis graph and a set of undesirable substances. Furthermore, information for substances is provided as metadata regarding their availability from the inventory. The SGP algorithm operates with a simple local rule set, in order to determine which nodes and edges need to be eliminated from the synthesis graph. In this study, we present the SGP algorithm in detail and provide several case studies that demonstrate the operation of the SGP algorithm. We believe that the SGP algorithm will be an essential component of computer aided synthesis planning.
format Online
Article
Text
id pubmed-9093600
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher American Chemical Society
record_format MEDLINE/PubMed
spelling pubmed-90936002023-04-19 Algorithm for the Pruning of Synthesis Graphs Zahoránszky-Kőhalmi, Gergely Lysov, Nikita Vorontcov, Ilia Wang, Jeffrey Soundararajan, Jeyaraman Metaxotos, Dimitrios Mathew, Biju Sarosh, Rafat Michael, Samuel G. Godfrey, Alexander G. J Chem Inf Model [Image: see text] Synthesis route planning is in the core of chemical intelligence that will power the autonomous chemistry platforms. In this task, we rely on algorithms to generate possible synthesis routes with the help of retro- and forward-synthetic approaches. Generated synthesis routes can be merged into a synthesis graph which represents theoretical pathways to the target molecule. However, it is often required to modify a synthesis graph due to typical constraints. These constraints might include “undesirable substances”, e.g., an intermediate that the chemist does not favor or substances that might be toxic. Consequently, we need to prune the synthesis graph by the elimination of such undesirable substances. Synthesis graphs can be represented as directed (not necessarily acyclic) bipartite graphs, and the pruning of such graphs in the light of a set of undesirable substances has been an open question. In this study, we present the Synthesis Graph Pruning (SGP) algorithm that addresses this question. The input to the SGP algorithm is a synthesis graph and a set of undesirable substances. Furthermore, information for substances is provided as metadata regarding their availability from the inventory. The SGP algorithm operates with a simple local rule set, in order to determine which nodes and edges need to be eliminated from the synthesis graph. In this study, we present the SGP algorithm in detail and provide several case studies that demonstrate the operation of the SGP algorithm. We believe that the SGP algorithm will be an essential component of computer aided synthesis planning. American Chemical Society 2022-04-19 2022-05-09 /pmc/articles/PMC9093600/ /pubmed/35438992 http://dx.doi.org/10.1021/acs.jcim.1c01202 Text en Not subject to U.S. Copyright. Published 2022 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 Zahoránszky-Kőhalmi, Gergely
Lysov, Nikita
Vorontcov, Ilia
Wang, Jeffrey
Soundararajan, Jeyaraman
Metaxotos, Dimitrios
Mathew, Biju
Sarosh, Rafat
Michael, Samuel G.
Godfrey, Alexander G.
Algorithm for the Pruning of Synthesis Graphs
title Algorithm for the Pruning of Synthesis Graphs
title_full Algorithm for the Pruning of Synthesis Graphs
title_fullStr Algorithm for the Pruning of Synthesis Graphs
title_full_unstemmed Algorithm for the Pruning of Synthesis Graphs
title_short Algorithm for the Pruning of Synthesis Graphs
title_sort algorithm for the pruning of synthesis graphs
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9093600/
https://www.ncbi.nlm.nih.gov/pubmed/35438992
http://dx.doi.org/10.1021/acs.jcim.1c01202
work_keys_str_mv AT zahoranszkykohalmigergely algorithmforthepruningofsynthesisgraphs
AT lysovnikita algorithmforthepruningofsynthesisgraphs
AT vorontcovilia algorithmforthepruningofsynthesisgraphs
AT wangjeffrey algorithmforthepruningofsynthesisgraphs
AT soundararajanjeyaraman algorithmforthepruningofsynthesisgraphs
AT metaxotosdimitrios algorithmforthepruningofsynthesisgraphs
AT mathewbiju algorithmforthepruningofsynthesisgraphs
AT saroshrafat algorithmforthepruningofsynthesisgraphs
AT michaelsamuelg algorithmforthepruningofsynthesisgraphs
AT godfreyalexanderg algorithmforthepruningofsynthesisgraphs