To further corroborate our hypothesis that the choice of DFT method for computing QM descriptors has minimal impact on our analysis, we conducted a comparative study using B3LYP/def2-SVP. Figure demonstrates that the results obtained with B3LYP/def2-SVP are remarkably consistent with those generated using ωB97XD/def2-SVP, further confirming our hypothesis. , separated into two categories: (a) regression datasets (PDBbind-F, PDBbind-C, PDBbind-R) using RMSE as metric (lower the better), and (b) classification datasets (HIV and BACE) using ROC-AUC as metric (higher the better). The error bar is calculated as the standard deviation of test errors across 3 folds for HIV dataset, and 10 folds for all other datasets. QM descriptors can provide some benefit even for targets lacking strong physical relationships, particularly with limited training data. We further quantified the enhancements provided by QM descriptors across different 1 regression tasks using models trained on 100 downsampled data points, and the findings indicate that QM descriptors generally offer greater benefits for targets that exhibit a stronger correlation or intuitive relationship with the descriptors (Figure ). However, the advantage of employing QM descriptors tends to gradually diminish as more training data become available. This observation suggests that with an expanded dataset, model performance may increasingly depend on the relationship between fundamental molecular graph information and prediction targets, rather than additional descriptors. Consequently, computing and including QM descriptors may not be cost-effective in scenarios where the training set is already sufficiently large. However, the ability of QM descriptors to provide meaningful improvements to the prediction of many molecular properties, despite the difference in the nature and origin of data shown, highlights their versatility and potential to improve the performance of predictive models across various chemical domains, especially in scenarios where limited training data is available.