We present an attention-based Transformer model for automatic retrosynthesis
route planning. Our approach starts from reactants prediction of single-step
organic reactions for given products, followed by Monte Carlo tree search-based
automatic retrosynthetic pathway prediction. Trained on two datasets from the
United States patent literature, our models achieved a top-1 prediction
accuracy of over 54.6% and 63.0% with more than 95% and 99.6% validity rate of
SMILES, respectively, which is the best up to now to our knowledge. We also
demonstrate the application potential of our model by successfully performing
multi-step retrosynthetic route planning for four case products, i.e.,
antiseizure drug Rufinamide, a novel allosteric activator, an inhibitor of
human acute-myeloid-leukemia cells and a complex intermediate of drug
candidate. Further, by using heuristics Monte Carlo tree search, we achieved
automatic retrosynthetic pathway searching and successfully reproduced
published synthesis pathways. In summary, our model has achieved the
state-of-the-art performance on single-step retrosynthetic prediction and
provides a novel strategy for automatic retrosynthetic pathway planning.

该研究提出了一种基于 Transformer 的自动回流合成路径规划模型，能够从受体预测开始，通过蒙特卡罗树搜索实现自动化回溯合成路径规划，经过两个数据集训练之后，其表现为单步回流合成预测的最佳，达到了 54.6％和 63.0％的准确性，尤其是其能够成功地进行多步回流合成路径规划，提供了全新的自动化回流合成规划策略。