In this study, we systematically explored various approaches for utilizing low-level computational data to enhance the performance of GNN models in predicting activation energies. These approaches differ in both the types of properties calculated using lowlevel methods and the ways in which these data are integrated into the model during training and prediction. To minimize computational costs associated with data generation, we focused on SQM methods, which are significantly more efficient than higher-level methods like DFT. Among the SQM methods we examined-AM1, PM3, and GFN2-xTB-we found that GFN2-xTB provided the lowest error in activation energy calculations. Consequently, GFN2-xTB was selected to generate the low-level data used in this study. The primary objective was to determine the most effective way to leverage these low-cost, low-level calculations to improve model performance, particularly when the availability of high-level data is limited.