  Reaction prediction is a fundamental problem in computational chemistry.
Existing approaches typically generate a chemical reaction by sampling tokens
or graph edits sequentially, conditioning on previously generated outputs.
These autoregressive generating methods impose an arbitrary ordering of outputs
and prevent parallel decoding during inference. We devise a novel decoder that
avoids such sequential generating and predicts the reaction in a
Non-Autoregressive manner. Inspired by physical-chemistry insights, we
represent edge edits in a molecule graph as electron flows, which can then be
predicted in parallel. To capture the uncertainty of reactions, we introduce
latent variables to generate multi-modal outputs. Following previous works, we
evaluate our model on USPTO MIT dataset. Our model achieves both an order of
magnitude lower inference latency, with state-of-the-art top-1 accuracy and
comparable performance on Top-K sampling.

Chemical Physics;Machine Learning