Prediction of 19F NMR chemical shift by machine learning

Abstract

Fluorine-19 (19F) is a nucleus of great importance in the field of Nuclear Magnetic Resonance (NMR) spectroscopy due to its high receptivity and wide chemical shift dispersion. 19F NMR plays crucial roles in both organic synthesis and biomedicine. Herein, a machine learning-based comprehensive 19F NMR chemical shift prediction model was established based on the experimental 19F NMR dataset from the book by Dolbier and the open NMR database nmrshiftdb2. Fluorine radical SMILES (Fr-SMILES) that reflected the fluorine chemical equivalence, was designed as the representation of fluorine in the molecule. Model trained with the graph convolution network (GCN) algorithm gave a low mean absolute error (MAE) of 3.636 ppm on the testing set. This model exhibits broad applicability and can effectively predict 19F NMR shifts for a wide range of organic fluorine molecules. We believe that the current work will provide a powerful tool for not only predicting 19F NMR shifts but also aiding in the analysis and identification of these shifts in diverse organic fluorine compounds. An online prediction platform was constructed based on the current model, which can be found at https://fluobase.cstspace.cn/fnmr.