<div><div><div><p>We present
an attention-based Transformer model for automatic retrosynthesis route planning.
Our approach starts from <a></a><a>reactants
prediction of single-step organic reactions for gi</a>ven products, <a>followed by Monte Carlo tree search-based
automatic retrosynthetic pathway prediction</a>. 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.
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Automatic Retrosynthetic Pathway Planning Using Template-free Models
