Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck

Benchmark Model Rank Results
graph-classification-on-mutagenicityS-CGIB#3Accuracy: 81.12±0.90
graph-classification-on-nci1S-CGIB#35Accuracy: 79.75±0.82
graph-classification-on-nci109S-CGIB#19Accuracy: 77.54±1.51
molecular-property-prediction-onS-CGIB#7RMSE: 0.762±0.042
molecular-property-prediction-on-bace-1S-CGIB#2ROC-AUC: 86.46±0.81
molecular-property-prediction-on-bbbp-1S-CGIB#8ROC-AUC: 88.75±0.49
molecular-property-prediction-on-clintox-1S-CGIB#11ROC-AUC: 78.58±2.01
molecular-property-prediction-on-esolS-CGIB#15RMSE: 0.816±0.019
molecular-property-prediction-on-freesolvS-CGIB#14RMSE: 1.648±0.074
molecular-property-prediction-on-hiv-1S-CGIB#4ROC-AUC: 78.33±1.34
molecular-property-prediction-on-muv-1S-CGIB#3ROC-AUC: 77.71±1.19
molecular-property-prediction-on-sider-1S-CGIB#10ROC-AUC: 64.03±1.04
molecular-property-prediction-on-tox21-1S-CGIB#3ROC-AUC: 80.94±0.17
molecular-property-prediction-on-toxcast-1S-CGIB#1ROC-AUC: 70.95±0.27