Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning

Accurate extraction of multiplet parameters, such as J-couplings and chemical shifts, play a vital role in small molecule analysis using nuclear magnetic resonance (NMR) spectroscopy. These parameters provide essential quantitative information about molecular structures, interatomic interactions, and chemical environments, enabling precise characterization of small organic compounds. This poster presents an innovative omputational approach that utilizes state-of-the-art deep learning techniques, specifically detection transformers, to automate and optimize the extraction of multiplet parameters from 1D NMR spectra of small molecules. By incorporating these advanced computational methods, experimenters can achieve improved efficiency, accuracy, and speed in analyzing and characterizing small organic compounds using NMR spectroscopy.