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Convolutional Networks on Graphs for Learning Molecular Fingerprints
David Duvenaud · Dougal Maclaurin · Jorge Iparraguirre · Rafael Bombarell · Timothy Hirzel · Alan Aspuru-Guzik · Ryan Adams

Thu Dec 10 08:00 AM -- 12:00 PM (PST) @ 210 C #10

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.

Author Information

David Duvenaud (University of Toronto)
Dougal Maclaurin (Harvard University)
Jorge Iparraguirre (Harvard University)
Rafael Bombarell (Harvard University)
Timothy Hirzel (Harvard University)
Alan Aspuru-Guzik (Harvard University)
Ryan Adams (Harvard)

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