GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts

Abstract

Abstract In this work, inspired by the graph transformer, we presented an improved protocol, termed GT-NMR, which integrates 2D molecular graph representation with Transformer architecture, for accurate yet efficient prediction of NMR chemical shifts. The effectiveness of the GT-NMR was thoroughly examined with the standard nmrshiftdb2 dataset, 37 natural products and structural elucidation of 11 pairs of natural products. Systematical analysis affirms that GT-NMR outperforms traditional graph-based methods in all aspects, achieving state-of-the-art performance, with the mean absolute error of 0.158 and 1.189 ppm in predicting 1H and 13C NMR chemical shifts, respectively, for the standard nmrshiftdb2 dataset. Further scrutiny of its practical applications indicates that GT-NMR's efficacy is closely tied to molecular complexity, as quantified by the size-normalized spatial score (nSPS). For relatively simple molecules (nSPS = 38.42). This trend is consistent across other graph-based NMR chemical shift prediction methods as well. Therefore, while employing GT-NMR or other graph-based methods for the rapid and routine prediction of NMR chemical shifts, it is advisable to utilize nSPS to assess their suitability. The source codes and trained model of GT-NMR are publicly available at GitHub. Scientific contribution GT-NMR, which combines the 2D molecular graph representation with the Transformer architecture, was implemented for the first time to predict atom-level NMR chemical shifts, achieving state-of-the-art performance. More importantly, the reliability of the GT-NMR and graph-based methods was assessed for the first time in terms of molecular complexity, as quantified by the size-normalized spacial score (nSPS). Systematical scrutiny demonstrated that GT-NMR offer a valuable way for routine application in structural screening and elucidation of relatively simple molecules.

Keywords

• NMR chemical shifts
• Machine learning
• Graph transformer
• Transformer
• Graph neural network
• Molecular complexity