In this paper, we present a data-driven method for the uncertainty-aware prediction of chemical reaction yields. The reactants and products in a chemical reaction are represented as a set of molecular graphs. The predictive distribution of the yield is modeled as a graph neural network that directly processes a set of graphs with permutation invariance. Uncertainty-aware learning and inference are applied to the model to make accurate predictions and to evaluate their uncertainty. We demonstrate the effectiveness of the proposed method on benchmark datasets with various settings. Compared to the existing methods, the proposed method improves the prediction and uncertainty quantification performance in most settings.

中文翻译：

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使用图神经网络对化学反应产率进行不确定性预测

在本文中，我们提出了一种数据驱动的方法，用于预测化学反应产率的不确定性。化学反应中的反应物和产物用一组分子图表示。产量的预测分布被建模为一个图神经网络，它直接处理一组具有排列不变性的图。将不确定性感知学习和推理应用于模型以做出准确的预测并评估其不确定性。我们证明了所提出的方法在具有各种设置的基准数据集上的有效性。与现有方法相比，所提出的方法在大多数情况下提高了预测和不确定性量化性能。