For each splitter, we conducted independent two-sample t-tests, with a significance level of α = 0.01 to determine whether there was a statistically significant difference between the behavior of classical ML and GNN. We found that for the scaffold, scaffold generic, molecular weight reverse, and K-means splitters, there was a significant difference between classical ML and GNN performance (p = 2 × 10 -3 , 5 × 10 -3 , 7 × 10 -5 , and 2 × 10 -3 , respectively). For all these cases, GNNs are more robust compared to classical ML models and exhibit a smaller decline in performance.