What question did this study set out to answer?

The aim is to develop a machine learning model to accurately predict NMR chemical shifts for large peptides.

May 9, 2026

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

Key Points

The aim is to develop a machine learning model to accurately predict NMR chemical shifts for large peptides.
Trained the machine learning model on experimental chemical shifts from large peptides.
Conducted comparison with a DFT-trained model to ensure accuracy.
Tested the model's predictions against experimental data.
Achieved strong correlation between DFT-computed and experimental chemical shifts.
Model demonstrated independence from training data sources.
Accurate predictions were possible for unknown large peptides.

Abstract

C 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.

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

Key Points

Abstract

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