  Reliably predicting the products of chemical reactions presents a fundamental
challenge in synthetic chemistry. Existing machine learning approaches
typically produce a reaction product by sequentially forming its subparts or
intermediate molecules. Such autoregressive methods, however, not only require
a pre-defined order for the incremental construction but preclude the use of
parallel decoding for efficient computation. To address these issues, we devise
a non-autoregressive learning paradigm that predicts reaction in one shot.
Leveraging the fact that chemical reactions can be described as a
redistribution of electrons in molecules, we formulate a reaction as an
arbitrary electron flow and predict it with a novel multi-pointer decoding
network. Experiments on the USPTO-MIT dataset show that our approach has
established a new state-of-the-art top-1 accuracy and achieves at least 27
times inference speedup over the state-of-the-art methods. Also, our
predictions are easier for chemists to interpret owing to predicting the
electron flows.
