Moreover, QM descriptors demonstrate a more substantial impact on model predictions compared to Chemprop's default features, highlighting the significant value of incorporating QM information into the models. Interestingly, despite the analysis being conducted on models trained with 80% of the dataset, a regime where the performance gap between QM-GNN and standard GNN models narrows (Figure ), the results reveal that QM descriptors continue to exert a meaningful influence on model predictions.