  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.
