To ensure the robustness and reliability of ML models, we implemented a rigorous data partitioning scheme. For CML models, the datasets were divided into 90% for training and validation with 9-fold cross-validation, and 10% for unbiased testing. For GNN models, we allocated 80% of each dataset for training, 10% for validation, and 10% for unbiased testing.