The structure-preserving spectral graph neural network for dual kinase inhibitors and synergy scoring in gastric cancer

The therapeutic targeting of kinase signaling pathways represents a pivotal strategy in gastric cancer, yet the rational design of single agents capable of dual-kinase inhibition remains a challenge in precision oncology. Here, we develop the DuoKinaseNet, a dual-task spectral graph neural network that integrates global topological information from a heterogeneous biomedical graph to enable structure-preserving prediction of drug-kinase interactions. The core innovation of our model is the Structure-Preserving Spectral Expansion (SPSE) module, which injects global graph topology from a biomedical knowledge graph into the learning process via spectral coordinates and diffusion-distance biased attention. Evaluated on a comprehensive dataset curated from DrugBank, DuoKinaseNet achieves state-of-the-art performance, particularly on the challenging "unseen protein" benchmark, with an AUC-ROC of 0.903 for HER2 and 0.895 for FGFR2b. It significantly outperforms a wide range of baseline models, including 3D-aware methods and single-task variants, empirically validating the synergistic benefits of the dual-task learning and SPSE frameworks.