  Supervised learning on molecules has incredible potential to be useful in
chemistry, drug discovery, and materials science. Luckily, several promising
and closely related neural network models invariant to molecular symmetries
have already been described in the literature. These models learn a message
passing algorithm and aggregation procedure to compute a function of their
entire input graph. At this point, the next step is to find a particularly
effective variant of this general approach and apply it to chemical prediction
benchmarks until we either solve them or reach the limits of the approach. In
this paper, we reformulate existing models into a single common framework we
call Message Passing Neural Networks (MPNNs) and explore additional novel
variations within this framework. Using MPNNs we demonstrate state of the art
results on an important molecular property prediction benchmark; these results
are strong enough that we believe future work should focus on datasets with
larger molecules or more accurate ground truth labels.
