Nevertheless, QM descriptors can provide crucial predictive insights in scenarios where gathering experimental data is slow and expensive, such as in design-make-test-analyze molecular discovery cycles. They can also be invaluable in navigating the complex landscape of chemistry in research areas often overwhelmed by the vast number of potential reaction pathways or molecules for screening, such as in retrosynthesis pathway exploration and high-throughput virtual screening of drug candidates. Furthermore, in active learning autonomous material design campaigns, the iterative nature of these projects means that even minor enhancements in model accuracy per cycle can culminate in significant improvements over time. Consequently, any marginal improvements offered by QM descriptors, especially in the early phases of molecular discovery projects, can markedly boost the effectiveness of the active learning and molecular synthesis loop. Therefore, in these diverse contexts, leveraging QM descriptors with D-MPNNs has the potential to be a valuable tool for innovative flow designed for high-throughput QM descriptors calculations is accessible through GitHub, . The customized Chemprop v1 capable of performing Shapley value analysis is available via oscarwumit/chemprop_developing/tree/shap_v1. Citations of dataset, scripts, and software programs should include reference to this article. Additional results and discussions can be found in the supporting information document.