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.
Authors
- Philippe Gantzer (ORCID: https://orcid.org/0000-0001-7494-458X)
- Tagir Akhmetshin (ORCID: https://orcid.org/0000-0002-2549-6431)
- Timur Madzhidov (ORCID: https://orcid.org/0000-0002-3834-6985)
- Alexandre Varnek (ORCID: https://orcid.org/0000-0003-1886-925X)
- Dmitry Babadeev (ORCID: https://orcid.org/0000-0001-9885-8037)
- Dmitry Zankov (ORCID: https://orcid.org/0000-0002-6201-3347)
- Anna Pinigina
Institutions
- Centre National de la Recherche Scientifique (FR)
- Hokkaido University (JP)
- RELX Group (United Kingdom) (GB)
- Université de Strasbourg (FR)
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