MIM-ML: Combining Molecular Fragmentation and Machine Learning for Accurate Prediction of NMR Chemical Shifts for...

Accurate prediction of NMR chemical shifts for large biomolecules remains a major challenge due to the steep scaling of quantum-mechanical (QM) models with system size. Our group has developed a systematic fragmentation-based approach known as molecules-in-molecules (MIM) that is applicable for large biomolecular systems and can calculate their NMR spectra accurately. Here, we developed a hybrid fragmentation-based machine learning framework (MIM-ML) for predicting ¹H and ¹³C NMR chemical shifts of large peptides. Low-cost single-layer MIM1-based DFT calculations with a microsolvation model were employed to generate the training data. For ML training, physics-based structural and electronic features were incorporated and derived from cost-efficient extended tight-binding (xTB) calculations. Our trained XGBR model using a relatively small amount of data (1406 for ¹H and 837 for ¹³C) achieved mean absolute deviations (MADs) of 0.33 ppm for ¹H and 2.91 ppm for ¹³C against experimental data for a test data set of large peptides. Furthermore, training the ML model directly on experimental chemical shifts produced results consistent with those of the DFT-trained model, indicating independence from the source of training data and a strong correlation between DFT-computed and experimental values. Overall, our developed ML model can be applied for the accurate prediction of the NMR chemical shifts for unknown large peptides.