In line with these recommendations, our study adopted a more rigorous assessment strategy by developing three levels of test sets, as shown in Figure . The "random split" involves randomly dividing the dataset into training and test sets, which, while ensuring exposure to a broad range of substrates and conditions, may still present overly optimistic results. To address this, we also created a "partial substrate novelty" test set, which excludes any test cases where both substrates were seen during training. This approach ensures that at least one novel substrate is present, offering a more challenging and realistic evaluation of the model's predictive capabilities. Finally, the "full substrate novelty" test set consists entirely of new substrate combinations that the model has not encountered during training, providing the most rigorous assessment of its generalizability. These three levels of testing-random split, partial substrate novelty, and full substrate novelty-offer a comprehensive framework to evaluate the model's robustness and applicability in real-world chemical spaces, aligning with the best practices recommended in recent literature.