CASCADE-2.0: Real Time Prediction of 13C-NMR Shifts with sub-ppm Accuracy

Nuclear Magnetic Resonance (NMR) is one of the most powerful structural characterization techniques in molecular sciences. However, the complexity of NMR spectra can make structural assignments prone to er-rors. Here we introduce a deep learning model – CASCADE-2.0 (ChemicAl Shift CAlculation with DEep learn-ing), a practical tool designed to assist chemists in making fast, reliable, and transparent 13C-NMR chemical shift predictions. Building on our previous model, we make improvements to the model architecture and train-ing data, while striving to enhance the model transparency. Leveraging advances in neural network poten-tials, a fourfold expansion of training data in terms of molecular and elemental coverage is made, resulting in a dataset containing around 170,000 experimental shifts cross-validated by DFT. To address DFT limitations, we developed an intelligent data augmentation strategy combining statistical analysis and machine learning predictions to further expand the dataset to 211,000 experimental values. With the expanded dataset and changes in model architecture, a state-of-the-art accuracy of 0.73 ppm was achieved when compared against experimental 13C-NMR shifts. The model also incorporates prediction confidence metrics using a deep-kernel learning architecture, as well as nearest-neighbor analysis, facilitated by a user-friendly web-server. Finally, we demonstrate the versatility of the final model using several real-world applications.