Moreover, strategically selecting a refined set of the most relevant descriptors is crucial, for it can improve model performance, enhance interpretability by potentially revealing underlying physicochemical principles governing the descriptor-target relationships, and reduce computational costs by avoiding unnecessary descriptor calculations. Finally, if opting for QM descriptors, it is advisable to conduct benchmarking against alternative methods (e.g., comparing QM-GNN with a standard GNN as a baseline). This ensures that the benefits gained from QM descriptors justify their added cost.