These results suggest that, despite Chemprop's proven success in predicting a wide range of molecular properties, it faces challenges in accurately predicting activation energies in our study. This suboptimal performance may be attributed to the limited amount of training data, which appears insufficient for effectively training the model, particularly when applied to a scaffold-split test set. In this context, the test set comprises molecular structures not encountered during training, making these predictions particularly challenging for the model. While increasing the amount of CCSD(T)-F12a training data could potentially improve model performance-an approach supported by the work of Spiekermann et al., who demonstrated that incorporating activation energies of reverse reactions can enhance model accuracywe chose not to expand the high-level data in this study. The primary goal of this work is to investigate the effectiveness of different approaches for incorporating low-level data. Therefore, we maintained a constant amount of high-level data without additional augmentation and focused on evaluating the efficacy of various low-level data integration strategies using the Chemprop model with the optimized hyperparameter set (Table ).