  Organic synthesis is one of the key stumbling blocks in medicinal chemistry.
A necessary yet unsolved step in planning synthesis is solving the forward
problem: given reactants and reagents, predict the products. Similar to other
work, we treat reaction prediction as a machine translation problem between
SMILES strings of reactants-reagents and the products. We show that a
multi-head attention Molecular Transformer model outperforms all algorithms in
the literature, achieving a top-1 accuracy above 90% on a common benchmark
dataset. Our algorithm requires no handcrafted rules, and accurately predicts
subtle chemical transformations. Crucially, our model can accurately estimate
its own uncertainty, with an uncertainty score that is 89% accurate in terms of
classifying whether a prediction is correct. Furthermore, we show that the
model is able to handle inputs without reactant-reagent split and including
stereochemistry, which makes our method universally applicable.
