Nuclear magnetic resonance (NMR) spectroscopy is one of the most important methods for analyzing the molecular structures of compounds. The objective in this study is to predict indirect spin-spin coupling constants in NMR based on machine learning. We propose important descriptors for predicting indirect spin-spin coupling constants from target pairs of atoms in molecules, and combine the proposed descriptors with molecular descriptors to predict indirect spin-spin coupling constants with Light-GBM as a regression analysis method. We construct regression models using a dataset and verify their predictive accuracy, and then confirm that the proposed descriptors can predict indirect spin-spin coupling constants more accurately than the traditional descriptors used to predict chemical shifts.
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