Convolutional Networks on Graphs for Learning Molecular Fingerprints

David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams

NeurIPS 2015 pp. 2224-2232

/neurips/2015/duvenaud2015neurips-convolutional/

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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Cite

Text

Duvenaud et al. "Convolutional Networks on Graphs for Learning Molecular Fingerprints." Neural Information Processing Systems, 2015.

Markdown

[Duvenaud et al. "Convolutional Networks on Graphs for Learning Molecular Fingerprints." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/duvenaud2015neurips-convolutional/)

BibTeX

@inproceedings{duvenaud2015neurips-convolutional,
  title     = {{Convolutional Networks on Graphs for Learning Molecular Fingerprints}},
  author    = {Duvenaud, David K. and Maclaurin, Dougal and Iparraguirre, Jorge and Bombarell, Rafael and Hirzel, Timothy and Aspuru-Guzik, Alan and Adams, Ryan P.},
  booktitle = {Neural Information Processing Systems},
  year      = {2015},
  pages     = {2224-2232},
  url       = {https://mlanthology.org/neurips/2015/duvenaud2015neurips-convolutional/}
}