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Bayesian Graph Neural Networks for Molecular Property Prediction

Lamb, G; Paige, B; (2020) Bayesian Graph Neural Networks for Molecular Property Prediction. In: Proceedings of the 2020 NeurIPS Workshop on Machine Learning for Molecules. Neural Information Processing Systems Green open access

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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.

Type: Proceedings paper
Title: Bayesian Graph Neural Networks for Molecular Property Prediction
Event: 2020 NeurIPS Workshop on Machine Learning for Molecules
Open access status: An open access version is available from UCL Discovery
Publisher version: https://ml4molecules.github.io/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: q-bio.BM, q-bio.BM, cs.LG
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10124712
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