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 < = 27.71), GT-NMR performs comparably to the best density functional while its effectiveness significantly diminishes with complex molecules characterized by higher nSPS values (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.

中文翻译：

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GT-NMR：一种基于图形变压器的新型方法，用于准确预测 NMR 化学位移


在这项工作中，受图形转换器的启发，我们提出了一种改进的协议，称为 GT-NMR，它将二维分子图表示与 Transformer 架构集成在一起，用于准确而有效地预测 NMR 化学位移。使用标准 nmrshiftdb2 数据集、37 种天然产物和 11 对天然产物的结构解析，彻底研究了 GT-NMR 的有效性。系统分析证实，GT-NMR 在所有方面都优于传统的基于图的方法，实现了最先进的性能，在预测标准 nmrshiftdb2 数据集的 1H 和 13C NMR 化学位移时，平均绝对误差分别为 0.158 和 1.189 ppm。对其实际应用的进一步审查表明，GT-NMR 的功效与分子复杂性密切相关，由大小归一化空间评分 （nSPS） 量化。对于相对简单的分子 （nSPS < = 27.71），GT-NMR 的性能与最佳密度泛函相当，而对于具有较高 nSPS 值 （nSPS > = 38.42） 的复杂分子，其有效性显着降低。这种趋势在其他基于图形的 NMR 化学位移预测方法中也是一致的。因此，在采用 GT-NMR 或其他基于图形的方法快速和常规预测 NMR 化学位移时，建议使用 nSPS 来评估其适用性。GT-NMR 的源代码和训练模型在 GitHub 上公开提供。科学贡献 GT-NMR 将 2D 分子图表示与 Transformer 架构相结合，首次用于预测原子级 NMR 化学位移，实现了最先进的性能。 更重要的是，GT-NMR 和基于图形的方法的可靠性首次根据分子复杂性进行了评估，由大小归一化空间评分 （nSPS） 量化。系统审查表明，GT-NMR 为相对简单分子的结构筛选和解析的常规应用提供了一种有价值的方法。