  Graph neural networks for molecular property prediction are frequently
underspecified by data and fail to generalise to new scaffolds at test time. A
potential solution is Bayesian learning, which can capture our uncertainty in
the model parameters. This study benchmarks a set of Bayesian methods applied
to a directed MPNN, using the QM9 regression dataset. We find that capturing
uncertainty in both readout and message passing parameters yields enhanced
predictive accuracy, calibration, and performance on a downstream molecular
search task.

Biomolecules;Machine Learning