Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts

Abstract

The study of structure–spectrum relationships is essential for spectral interpretation, impacting structural elucidation and material design. Predicting spectra from molecular structures is challenging due to their complex relationships. Here we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pretraining and fine-tuning paradigm. To support the evaluation of nuclear magnetic resonance chemical shift prediction models, we have established a comprehensive benchmark based on previous research and databases, covering diverse chemical systems. Applying NMRNet to these benchmark datasets, we achieve competitive performance in both liquid-state and solid-state nuclear magnetic resonance datasets, demonstrating its robustness and practical utility in real-world scenarios. Our work helps to advance deep learning applications in analytical and structural chemistry. A deep learning framework (NMRNet) is developed to model atomic environments for predicting NMR chemical shifts. A benchmark dataset, nmrshiftdb2-2024, is also established to provide a standardized source for evaluating NMR models.