Convolutional Networks on Graphs for Learning Molecular Fingerprints

09/30/2015 ∙ by David Duvenaud, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

Code Repositories

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.