Forward Reaction Prediction as Reverse Verification: A Novel Approach to Retrosynthesis

Pupils' intuitive knowledge can lead them to verify multiplication by means of division. Based on this analogy, this study introduces the basic reverse verification concept to verify retrosynthesis through forward reaction. In this work, we present a "combined" model approach for retrosynthetic reaction prediction, where the first model is applied to retrosynthesis, and the second model, which is a "verified" model, is applied to the forward reaction prediction to verify the top-n reactants predicted by the retrosynthetic model. Using a "combined" model borrowed from human language translation, sequence-to-sequence (seq2seq) + transformer models, we improve the top-1 accuracy of retrosynthetic prediction by 4.3% (37.4% vs 41.7%). The application of the similarity + seq2seq models increases the top-1 accuracy by 4.6% (52.9% vs 57.5%). In this way, we can not only improve the accuracy but also automate the evaluation of the synthetic route.