Ultimately, the applicability of this and similar approaches of integrating machine learning approaches in predictive modeling of computationally costly calculations will certainly enhance modern efforts in improving efficiency and accuracy of targeted predictions of molecular properties. In the future, integrating Graph Neural Networks (GNNs) to predict 19 F NMR chemical shifts, leveraging their ability to learn from the 3D molecular structures, may further improve DFT-level accuracy. Additionally, developing machine learning models trained directly on experimental NMR data could expand the applicability of these models beyond DFT-calculated shifts. Such efforts are currently underway in our group and will be reported in due course of time.