Convolutional Networks on Graphs for Learning Molecular Fingerprints
Machine Learning
2015-11-04 v2 Neural and Evolutionary Computing
Machine Learning
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.
Cite
@article{arxiv.1509.09292,
title = {Convolutional Networks on Graphs for Learning Molecular Fingerprints},
author = {David Duvenaud and Dougal Maclaurin and Jorge Aguilera-Iparraguirre and Rafael Gómez-Bombarelli and Timothy Hirzel and Alán Aspuru-Guzik and Ryan P. Adams},
journal= {arXiv preprint arXiv:1509.09292},
year = {2015}
}
Comments
9 pages, 5 figures. To appear in Neural Information Processing Systems (NIPS)