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

Tagir Akhmetshin1, Dmitry Zankov2, Philippe Gantzer2

  • 1Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 4, Blaise Pascal Str., 67000 Strasbourg, France.

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|December 31, 2024
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Summary

SynPlanner is a flexible tool for creating custom retrosynthetic planning models from reaction data. It streamlines the entire process, from data curation to planning, enabling tailored solutions for chemical synthesis.

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Area of Science:

  • Computational Chemistry
  • Chemoinformatics
  • Organic Synthesis

Background:

  • Retrosynthetic analysis is crucial for planning chemical synthesis.
  • Developing customized retrosynthetic planners requires robust tools and methodologies.
  • Existing tools may lack the flexibility needed for diverse synthetic challenges.

Purpose of the Study:

  • To introduce SynPlanner, an end-to-end tool for designing customized retrosynthetic planners.
  • To provide a flexible platform supporting various stages of retrosynthetic planning development.
  • To enhance the efficiency and accessibility of creating bespoke retrosynthetic solutions.

Main Methods:

  • A comprehensive reaction data curation pipeline including atom-to-atom mapping, standardization, and filtration.
  • Reaction rule extraction and retrosynthetic model training.
  • Retrosynthetic planning utilizing Monte Carlo Tree Search (MCTS) guided by graph neural networks.

Main Results:

  • SynPlanner offers a customizable pipeline for building tailored retrosynthetic planning tools.
  • The tool integrates reaction data processing, rule extraction, model training, and planning.
  • Graph neural networks guide MCTS for accurate retrosynthetic rule predictions and precursor synthesizability assessment.

Conclusions:

  • SynPlanner provides a flexible and comprehensive solution for developing customized retrosynthetic planners.
  • The tool's modular design allows adaptation to specific research and development needs.
  • Accessible via GUI and CLI, SynPlanner is supported by tutorials and available on GitHub.