AGRL-DSE: Adaptive Graph Representation Learning on a Heterogeneous Graph for Drug Side Effect Prediction

Identifying side effects is crucial for drug development and postmarket surveillance. Several computational methods based on graph neural networks (GNNs) have been developed, leveraging the topological structure and node attributes in graphs with promising results. However, existing heterogeneous-network-based approaches often fail to fully capture the complex structure and rich semantic information within these networks. Furthermore, the oversmoothing problem in GNNs remains a major challenge. In this study, we propose AGRL-DSE, a novel adaptive graph representation learning framework designed to enhance node-feature learning for predicting drug side effects. First, we construct a heterogeneous graph with intra- and interlayer connections to represent similarities and associations between drugs and side effects, capturing hidden topological relationships in heterogeneous contexts. Second, we integrate three GNN modules in AGRL-DSE, graph convolutional network (GCN), graph sample and aggregation (GraphSAGE), and graph attention network (GAT) at the graph, node, and edge levels, respectively, with the aim of capturing semantic information at different levels in graph data in a hierarchical manner, gradually extracting and enhancing the features of the graph. Additionally, we introduce an adaptive layer attention mechanism that dynamically assigns weights to each layer's features to achieve adaptive fusion of multilevel features, thereby automatically adjusting the contribution of each layer to the final embedding. Experimental results demonstrate that AGRL-DSE outperforms state-of-the-art predictive models in both hot- and cold-start scenarios, highlighting its superiority and generalizability. AGRL-DSE's ability to capture complex relationships and provide deeper insights into drug-side effect interactions could transform drug evaluation, monitoring, and prescription, leading to better health outcomes and more efficient drug development processes.