Publication: Convolutional Networks on Graphs for Learning Molecular Fingerprints.
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Date
2015
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Neural Information Processing Systems Foundation, Inc.
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Duvenaud, David, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafa Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams. 2015. Convolutional Networks on Graphs for Learning Molecular Fingerprints. In the Proceedings of Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, Canada, Decemeber 7-12, 2015: 2215-2223.
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.
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