Learning single-step retrosynthesis with pseudo-reactions

Retrosynthesis analysis aims to design reaction pathways and intermediates for a target compound. Emerging works have been developed to automate this process using artificial intelligence (AI). Although there are multiple synthesis pathways to synthesize one target product, most of the existing models were optimized to select only one of them. One of the potential reason is due to the absence of other potential reactions in the available reaction dataset, as one product usually only appears one time in the reaction dataset. In this work, we generate virtually validated pseudo-reactions using local reaction templates and reaction outcome prediction model to optimize the retrosynthesis model to predict multiple synthesis pathways. With the aid of newly generated pseudo-reactions, the top-10 exact match accuracy is increased from 93.1% to 94.2% and the top-10 round trip accuracy is increased from 81.3% to 87.6% with higher prediction confidence and diversity on a public reaction dataset.