论文标题
分子图的分层间传递在分子图上学习
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
论文作者
论文摘要
我们提出了一个分层神经信息传递结构，用于在分子图上学习。我们的模型采用了两个互补的图表示：原始分子图表示及其相关的连接树，其中节点代表原始图中有意义的簇，例如环或桥接化合物。然后，我们通过在每个图内传递消息来学习分子的表示形式，并使用粗到五个表示的信息流在两个表示之间交换消息。我们的方法能够克服经典GNN已知的一些限制，例如检测周期，同时仍然非常有效地训练。我们在锌数据集和基于Moleculenet基准集合的数据集中验证其性能。
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.
