Nuclear Magnetic Resonance Chemical Shift

Nuclear magnetic resonance (NMR) chemical shift prediction aims to accurately forecast the resonance frequencies of atomic nuclei within molecules based on their structure, a crucial step in interpreting NMR spectra. Current research heavily utilizes machine learning, particularly graph neural networks and deep learning models like Deep Image Prior, to improve prediction accuracy and efficiency, even with limited datasets, focusing on challenging molecules like carbohydrates and heteronuclear systems. These advancements are significantly impacting various fields, including metabolomics, structural biology, and drug discovery, by accelerating molecular structure elucidation and analysis.