Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Network with Group Lasso Regularization

) and explain property differences. To enhance model explainability, we trained GNNs with structure-aware loss functions using group lasso and sparse group lasso regularizations, which prune and highlight molecular subgraphs relevant to activity differences. We applied this framework to the activity cliff data of molecules targeting 6 tyrosine-protein kinases across Src, Abl, and Tec families, as well as anaplastic lymphoma kinase. Integrating common- and uncommon-node information with sparse group lasso improves molecular property prediction for specific protein targets, as evidenced by lower root mean square errors and higher Pearson's correlation coefficients. Applying regularizations also enhances feature attribution for GNNs by boosting graph-level global direction scores and improving atom-level coloring accuracy. These advances strengthen model interpretability in drug discovery pipelines, particularly in identifying critical molecular substructures in lead optimization.