论文标题
用于药物相互作用预测的双级图形神经网络
Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction
论文作者
论文摘要
我们介绍了BI-GNN，用于建模生物学联系预测任务，例如药物 - 药物相互作用（DDI）和蛋白质 - 蛋白质相互作用（PPI）。以药物相互作用为例，使用机器学习的现有方法仅利用药物之间的链接结构，而无需使用每个药物分子的图表，或者仅利用单个药物化合物结构而不使用图形结构来用于高级DDI图。 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高级互动图和低级表示图，但也为未来的研究机会提供了一个基线，以解决数据的双层性质。
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.
