论文标题
用于分子财产预测的贝叶斯图神经网络
Bayesian Graph Neural Networks for Molecular Property Prediction
论文作者
论文摘要
用于分子属性预测的图形神经网络经常被数据指定，并且在测试时未能推广到新的脚手架。潜在的解决方案是贝叶斯学习，它可以在模型参数中捕获我们的不确定性。这项研究基准了使用QM9回归数据集应用于有向MPNN的一组贝叶斯方法。我们发现，捕获读数和消息传递参数中的不确定性可在下游分子搜索任务上产生增强的预测精度，校准和性能。
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.
