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David Duvenaud; Dougal Maclaurin; Jorge Aguilera-Iparraguirre; Rafael Gómez-Bombarelli; Timothy Hirzel; Alán Aspuru-Guzik; Ryan P. Adams
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.
Code Repositories
onakanob/Peptide_Graph_Autograd
Mentioned in GitHub
SystemicCypher/Neural-Molecule-Fingerprints
tf
Mentioned in GitHub
kimisyo/simple-GCN
pytorch
Mentioned in GitHub
nrel/m2p
Mentioned in GitHub
pgniewko/solubility
Mentioned in GitHub
HIPS/neural-fingerprint
Official
tf
Mentioned in GitHub
debbiemarkslab/neural-fingerprint-theano
Mentioned in GitHub
Sarikaya-Lab-GEMSEC/Peptide_Graph_Autograd
Mentioned in GitHub
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| drug-discovery-on-hiv-dataset | GraphConv | AUC: 0.822 |
| drug-discovery-on-muv | GraphConv | AUC: 0.836 |
| drug-discovery-on-pcba | GraphConv | AUC: 0.855 |
| drug-discovery-on-tox21 | GraphConv | AUC: 0.846 |
| drug-discovery-on-toxcast | GraphConv | AUC: 0.754 |
| graph-regression-on-lipophilicity | GC | RMSE: 0.655 |
| node-classification-on-citeseer-05 | GCN-FP | Accuracy: 43.9% |
| node-classification-on-citeseer-1 | GCN-FP | Accuracy: 54.3% |
| node-classification-on-citeseer-with-public | GCN-FP | Accuracy: 61.5% |
| node-classification-on-cora-05 | GCN-FP | Accuracy: 50.5% |
| node-classification-on-cora-1 | GCN-FP | Accuracy: 59.6% |
| node-classification-on-cora-3 | GCN-FP | Accuracy: 71.7% |
| node-classification-on-cora-with-public-split | GCN-FP | Accuracy: 74.6% |
| node-classification-on-pubmed-003 | GCN-FP | Accuracy: 56.2% |
| node-classification-on-pubmed-005 | GCN-FP | Accuracy: 63.2% |
| node-classification-on-pubmed-01 | GCN-FP | Accuracy: 70.3% |
| node-classification-on-pubmed-with-public | GCN-FP | Accuracy: 76.0% |
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