SynPlanner: an end-to-end tool for synthesis planning

SynPlanner is an open-source tool for retrosynthetic planning, designed to increase flexibility in training and developing customized retrosynthetic planning solutions from raw chemical data. It integrates Monte Carlo Tree Search (MCTS) with graph neural networks to evaluate applicable reaction rules (policy network) and the synthesizability of intermediate products (value network). SynPlanner can be used directly with pre-trained policy/value networks or fine-tuned on custom data through an automated end-to-end training pipeline. Additionally, SynPlanner enables the training of custom value functions on discovered synthesis routes to improve predictive performance. The tool includes original modules for atom-to-atom mapping, reaction curation, standardization, and extraction of reaction rules, ensuring the reproducibility of the training pipeline from initial data to trained retro-synthetic models. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner