Bayesian Graph Neural Networks for Molecular Property Prediction

• URL: http://arxiv.org/abs/2012.02089v1
• Date: Wed, 25 Nov 2020 22:32:54 GMT
• Title: Bayesian Graph Neural Networks for Molecular Property Prediction
• Authors: George Lamb, Brooks Paige
• Abstract summary: 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.
• Score: 15.160090982544867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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