  Chemical reactions can be described as the stepwise redistribution of
electrons in molecules. As such, reactions are often depicted using
`arrow-pushing' diagrams which show this movement as a sequence of arrows. We
propose an electron path prediction model (ELECTRO) to learn these sequences
directly from raw reaction data. Instead of predicting product molecules
directly from reactant molecules in one shot, learning a model of electron
movement has the benefits of (a) being easy for chemists to interpret, (b)
incorporating constraints of chemistry, such as balanced atom counts before and
after the reaction, and (c) naturally encoding the sparsity of chemical
reactions, which usually involve changes in only a small number of atoms in the
reactants.We design a method to extract approximate reaction paths from any
dataset of atom-mapped reaction SMILES strings. Our model achieves excellent
performance on an important subset of the USPTO reaction dataset, comparing
favorably to the strongest baselines. Furthermore, we show that our model
recovers a basic knowledge of chemistry without being explicitly trained to do
so.
