We present a hierarchical neural message passing architecture for learning on molecular graphs. Our model takes in two complementary graph representations: the raw molecular graph representation and its associated junction tree, where nodes represent meaningful clusters in the original graph, e.g., rings or bridged compounds. We then proceed to learn a molecule's representation by passing messages inside each graph, and exchange messages between the two representations using a coarse-to-fine and fine-to-coarse information flow. Our method is able to overcome some of the restrictions known from classical GNNs, like detecting cycles, while still being very efficient to train. We validate its performance on the ZINC dataset and datasets stemming from the MoleculeNet benchmark collection.

翻译：我们展示了一个等级神经信息传递结构,用于在分子图上学习。我们的模型以两个互补的图表表示:原始分子图代表及其相关的连接线树,其中节点代表原始图中有意义的组群,例如环或桥状化合物。然后我们通过在每个图内传递信息,在两个表达体之间交流信息,使用粗略到细微到粗略的信息流,我们的方法能够克服古典GNN的一些限制,如探测周期,同时仍然非常高效地培训。我们验证了它在ZINC数据集和来自MoleculeNet基准收集的数据集上的性能。