SynPlanner: An End-to-End Tool for Synthesis Planning

SynPlanner is an end-to-end tool for designing customized retrosynthetic planners from reaction data. It includes a reaction data curation pipeline (reaction atom-to-atom mapping, reaction standardization, and filtration), reaction rule extraction, retrosynthetic model training, and retrosynthetic planning. The tool is designed to be as flexible as possible, supporting the customization of each step of the pipeline to address different needs in the development of customized retrosynthetic planning solutions. The retrosynthetic planning in SynPlanner is performed by Monte Carlo Tree Search (MCTS) guided by graph neural networks for node expansion (retrosynthetic rule predictions) and evaluation (precursor synthesizability prediction). The solution can be accessed by a simple graphical user interface and a command line interface and is accompanied by a collection of tutorials. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2024-12-31
DOI
https://doi.org/10.1021/acs.jcim.4c02004
Citations
4
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.47
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article

SynPlanner: An End-to-End Tool for Synthesis Planning

Philippe Gantzer, Tagir Akhmetshin, Timur Madzhidov, Alexandre Varnek et al.
4 citations
Journal of Chemical Information and Modeling
Machine Learning in Materials Science
0.47
article

SynPlanner: An End-to-End Tool for Synthesis Planning

Philippe Gantzer, Tagir Akhmetshin, Timur Madzhidov, Alexandre Varnek, Dmitry Babadeev, Dmitry Zankov, Anna Pinigina
article en
4 citations

Abstract

SynPlanner is an end-to-end tool for designing customized retrosynthetic planners from reaction data. It includes a reaction data curation pipeline (reaction atom-to-atom mapping, reaction standardization, and filtration), reaction rule extraction, retrosynthetic model training, and retrosynthetic planning. The tool is designed to be as flexible as possible, supporting the customization of each step of the pipeline to address different needs in the development of customized retrosynthetic planning solutions. The retrosynthetic planning in SynPlanner is performed by Monte Carlo Tree Search (MCTS) guided by graph neural networks for node expansion (retrosynthetic rule predictions) and evaluation (precursor synthesizability prediction). The solution can be accessed by a simple graphical user interface and a command line interface and is accompanied by a collection of tutorials. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner.

Journal of Chemical Information and ModelingVol. 65(1)
Centre National de la Recherche Scientifique (FR), Hokkaido University (JP), RELX Group (United Kingdom) (GB), Université de Strasbourg (FR)
Sustainable cities and communities
Openalex Percentile: Top 37%
Machine Learning in Materials Science
0.47
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SynPlanner: An End-to-End Tool for Synthesis Planning — Philippe Gantzer, Tagir Akhmetshin, et al. · Journal of Chemical Information and Modeling (2024) | TGRS Research Map | TGRS