Convolutional Networks on Graphs for Learning Molecular Fingerprints
This work addresses the challenge of molecular property prediction for chemists and drug discovery, offering a more interpretable and effective alternative to traditional methods.
The authors tackled the problem of learning molecular fingerprints by introducing a convolutional neural network that operates directly on graphs, enabling end-to-end learning for arbitrary graph inputs and showing improved predictive performance on various tasks.
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.