  We introduce a convolutional neural network that operates directly on graphs.
These networks allow end-to-end learning of prediction pipelines whose inputs
are graphs of arbitrary size and shape. The architecture we present generalizes
standard molecular feature extraction methods based on circular fingerprints.
We show that these data-driven features are more interpretable, and have better
predictive performance on a variety of tasks.
