Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts

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. Herein, we introduce NMRNet, a deep learning framework using t...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Xu, Fanjie, Guo, Wentao, Wang, Feng, Yao, Lin, Wang, Hongshuai, Tang, Fujie, Gao, Zhifeng, Zhang, Linfeng, Weinan, E, Zhong-Qun Tian, Cheng, Jun
Format: Article
Language:English
Subjects:
Benchmarks
Chemical equilibrium
Datasets
Deep learning
Environment models
Molecular structure
NMR
Nuclear magnetic resonance
Prediction models
Predictions
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Summary: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. Herein, we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pre-training and fine-tuning paradigm. To support the evaluation of NMR 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 state-of-the-art performance in both liquid-state and solid-state NMR datasets, demonstrating its robustness and practical utility in real-world scenarios. This marks the first integration of solid and liquid state NMR within a unified model architecture, highlighting the need for domainspecific handling of different atomic environments. Our work sets a new standard for NMR prediction, advancing deep learning applications in analytical and structural chemistry.
ISSN:2331-8422