HackerPapers

arXiv:1509.09292cs.LG

Convolutional Networks on Graphs for Learning Molecular Fingerprints

David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

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.

arXiv comments
9 pages, 5 figures. To appear in Neural Information Processing Systems (NIPS)
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.NENeural and Evolutionary Computing stat.MLMachine Learning (Statistics)

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Apr 2019 to Apr 2019.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. Graph Convolutional Networks (2016) 1 comment

    • Yes. This problem is of particular importance in drug response prediction, where small molecules are represented as graphs where the atoms are nodes and the bonds are edges. These are difficult to work with because of the variability in the number of nodes. [1]…

      wjn0View on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.