We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI). Taking drug-drug interaction as an example, existing methods using machine learning either only utilize the link structure between drugs without using the graph representation of each drug molecule, or only leverage the individual drug compound structures without using graph structure for the higher-level DDI graph. The key idea of our method is to fundamentally view the data as a bi-level graph, where the highest level graph represents the interaction between biological entities (interaction graph), and each biological entity itself is further expanded to its intrinsic graph representation (representation graphs), where the graph is either flat like a drug compound or hierarchical like a protein with amino acid level graph, secondary structure, tertiary structure, etc. Our model not only allows the usage of information from both the high-level interaction graph and the low-level representation graphs, but also offers a baseline for future research opportunities to address the bi-level nature of the data.

中文翻译：

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用于药物相互作用预测的双层图神经网络

我们引入 Bi-GNN 来对生物链接预测任务进行建模，例如药物-药物相互作用 (DDI) 和蛋白质-蛋白质相互作用 (PPI)。以药物-药物相互作用为例，现有的机器学习方法要么仅利用药物之间的链接结构，而不使用每个药物分子的图表示，要么仅利用单个药物化合物结构而不使用图结构进行更高级别的 DDI图形。我们方法的关键思想是从根本上将数据视为一个双层图，其中最高层图表示生物实体之间的交互（交互图），每个生物实体本身进一步扩展为其内在图表示（表示图），