ADiffGDA: Exploring Gene-Drug Associations via Adaptive Graph Diffusion Networks

Exploring gene-drug associations is a key step in identifying new drug candidates, but traditional experimental methods are often expensive and time-consuming. While Graph Neural Network (GNN)-based models have demonstrated effectiveness in association prediction tasks, they face challenges in the information aggregation process. Existing GNN models either treat all nodes uniformly or rely on simple attention mechanisms to assign weights to neighboring nodes, limiting their capacity to capture complex relationships and improve performance. To address these limitations, we propose a novel adaptive graph diffusion network, ADiffGDA, for gene-drug association prediction. The model begins by randomly initializing embeddings for genes and drugs, which are then updated through neighborhood information aggregation. A key feature of ADiffGDA is the incorporation of a heat kernel, enabling each node to dynamically adjust aggregation weights based on its local structure. This approach allows the model to better capture variations in node types and local neighborhood patterns. Through extensive comparative experiments, we show that ADiffGDA outperforms existing state-of-the-art methods. Furthermore, case studies validate its effectiveness as a predictive tool, offering valuable insights for future biological experiments. The code and datasets for ADiffGDA are freely available at https://github.com/one-melon/ADiffGDA.