A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculation with Density Functional Theory Level Performance for Structure Assignment

Nuclear magnetic resonance (NMR) chemical shift calculation is a powerful tool for structural elucidation, and has been extensively employed in both synthetic and natural product chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks. Herein, we have constructed a 54-layer deep graph convolutional network for 13C NMR chemical shift calculation, which achieved high accuracy with low time-cost, and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semi-empirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K rings junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculation to date. This model has been developed into a user-friendly software, providing a useful tool for routinary rapid structure validation and assignation, as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculation.