  Virtual screening aims to find desirable compounds from chemical library by
using computational methods. For this purpose with machine learning, model
outputs that can be interpreted as predictive probability will be beneficial,
in that a high prediction score corresponds to high probability of correctness.
In this work, we present a study on the prediction performance and reliability
of graph neural networks trained with the recently proposed Bayesian learning
algorithms. Our work shows that Bayesian learning algorithms allow
well-calibrated predictions for various GNN architectures and classification
tasks. Also, we show the implications of reliable predictions on virtual
screening, where Bayesian learning may lead to higher success in finding hit
compounds.
