  The concern of overconfident mis-predictions under distributional shift
demands extensive reliability research on Graph Neural Networks used in
critical tasks in drug discovery. Here we first introduce CardioTox, a
real-world benchmark on drug cardio-toxicity to facilitate such efforts. Our
exploratory study shows overconfident mis-predictions are often distant from
training data. That leads us to develop distance-aware GNNs: GNN-SNGP. Through
evaluation on CardioTox and three established benchmarks, we demonstrate
GNN-SNGP's effectiveness in increasing distance-awareness, reducing
overconfident mis-predictions and making better calibrated predictions without
sacrificing accuracy performance. Our ablation study further reveals the
representation learned by GNN-SNGP improves distance-preservation over its base
architecture and is one major factor for improvements.
