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arxiv: 1509.09292 · v2 · pith:QV4XVSDCnew · submitted 2015-09-30 · 💻 cs.LG · cs.NE· stat.ML

Convolutional Networks on Graphs for Learning Molecular Fingerprints

David Duvenaud , Dougal Maclaurin , Jorge Aguilera-Iparraguirre , Rafael G\'omez-Bombarelli , Timothy Hirzel , Al\'an Aspuru-Guzik , Ryan P. Adams This is my paper
classification 💻 cs.LG cs.NEstat.ML
keywords graphsconvolutionalfingerprintslearningmolecularnetworksallowarbitrary
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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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