Automatic Retrosynthetic Pathway Planning Using Template-free Models

TL;DR

This paper introduces an attention-based Transformer model combined with Monte Carlo tree search for automatic retrosynthesis, achieving state-of-the-art accuracy and validity in predicting multi-step synthetic routes for complex organic molecules.

Contribution

The paper presents a novel template-free, Transformer-based approach integrated with Monte Carlo tree search for retrosynthetic pathway planning, outperforming previous methods in accuracy and validity.

Findings

Top-1 prediction accuracy over 54.6% and 63.0%.

Validity rate of SMILES over 95% and 99.6%.

Successfully planned multi-step routes for complex drugs.

Abstract

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.