Abstract: Traditional experimental toxicity testing is costly, time-consuming, and raises ethical concerns, creating an urgent need for computational alternatives that can rapidly screen chemical safety while maintaining interpretability for regulatory acceptance. This study develops a hybrid framework combining Graph Attention Networks (GAT) with XGBoost for molecular toxicity prediction on the Tox21 benchmark dataset.