More specifically, we conduct a comprehensive exploration of the impact of QM descriptors on the performance of D-MPNN models to predict various important chemical properties across 16 distinct molecular property datasets, ranging from electronic energy and solubility to toxicity and protein binding affinity. The dataset sizes span from several hundred to hundreds of thousands of data points, and both classification and regression tasks are investigated. We adapt the work of Guan et al. to compute QM descriptors with high throughput and also implemented a flexible way of providing or predicting atom and bond QM descriptors in Chemprop, the leading open-source D-MPNN software for chemical property prediction. We compute and curate a computational dataset that contains 37 distinct QM descriptors for approximately 65,000 molecules using 6 different model chemistry, and subsequently train models to predict these descriptors efficiently. We conduct an assessment of the QM-GNN and ml-QM-GNN models across various prediction tasks. Based on our results, we make recommendations about when and how QM descriptors should be used. It should be noted that, while in the rest of the manuscript we adopt the same acronyms for ML models (e.g., QM-GNN, ml-QM-GNN) as Guan et al., this work uses D-MPNN as the chosen GNN, not the WLN model they used. The selection of appropriate methods depends on the availability of resources and the desired balance between accuracy and efficiency. In the QM-GNN approach, QM descriptors are directly calculated from quantum chemical simulations, while in the ml-QM-GNN approach, machine learning (ML) models are employed to estimate QM descriptors, offering a more computationally efficient alternative.