Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction
This work addresses drug-drug interaction prediction for biomedical research, offering a novel approach that integrates multiple graph levels, though it is incremental in advancing graph neural network applications.
The paper tackles the problem of predicting drug-drug interactions by introducing Bi-GNN, a method that models data as a bi-level graph to incorporate both interaction structures and intrinsic graph representations of biological entities, resulting in improved performance on benchmark datasets.
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.