  Template-free SMILES-to-SMILES translation models for reaction prediction and
single-step retrosynthesis are of interest for industrial applications in
computer-aided synthesis planning systems due to their state-of-the-art
accuracy. However, they suffer from slow inference speed. We present a method
to accelerate inference in autoregressive SMILES generators through speculative
decoding by copying query string subsequences into target strings in the right
places. We apply our method to the molecular transformer implemented in Pytorch
Lightning and achieve over 3X faster inference in reaction prediction and
single-step retrosynthesis, with no loss in accuracy.
