Recently, they also successfully applied this approach to predict activation and reaction energies for [3 + 2] cycloadditions, identifying promising candidates for bioorthogonal click reactions through active learning. These and other works indicate that QM descriptors can be beneficial for property prediction to varying degrees. However, contrasting findings from Spiekermann et al. showed that QM descriptors did not enhance activation energy prediction in a D-MPNN model trained on approximately 24,000 diverse gas-phase reactions. The authors suggested that the underperformance of QM-GNN may be attributed to the large training set, although the learning curve had not reached asymptotic behavior in the baseline GNN model without QM descriptors. Similar observations were made by Guan et al., who recently utilized Fukui indices for predicting the regioselectivity of nucleophilic aromatic substitution reactions from a newly compiled in-house dataset. Likewise, Biswas et al. incorporated Hirshfeld partial charges and Mulliken total dipole moment into their D-MPNN and graph attention network models for predicting the acentric factors and critical properties of fluids. Both studies concluded that the inclusion of QM descriptors had minimal impact on the overall performance of their respective models. Collectively, these studies indicate that while QM descriptors may be beneficial in certain scenarios, their effectiveness is highly context-dependent and seems to be particularly influenced by the size of the data set and the nature of the chemical prediction task.