Convolutional Networks on Graphs for Learning Molecular Fingerprints
David Duvenaud
and
Dougal Maclaurin
and
Jorge Aguilera-Iparraguirre
and
Rafael Gómez-Bombarelli
and
Timothy Hirzel
and
Alán Aspuru-Guzik
and
Ryan P. Adams
arXiv e-Print archive - 2015 via Local arXiv
Keywords:
cs.LG, cs.NE, stat.ML
First published: 2015/09/30 (8 years ago) Abstract: 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.
