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

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nature computational science Pub Date : 2025-04-01 Epub Date: 2025-03-28 DOI:10.1038/s43588-025-00783-z
Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang, Zhifeng Gao, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng
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引用次数: 0

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.

为基于深度学习的核磁共振化学位移预测建立统一的基准和框架。
结构-光谱关系的研究对光谱解释至关重要,影响着结构解析和材料设计。由于分子结构之间的复杂关系,从分子结构中预测光谱具有挑战性。在这里,我们介绍NMRNet,这是一个使用SE(3) Transformer进行原子环境建模的深度学习框架,遵循预训练和微调范例。为了支持核磁共振化学位移预测模型的评估,我们在前人研究和数据库的基础上建立了一个综合基准,涵盖了不同的化学体系。将NMRNet应用于这些基准数据集,我们在液态和固态核磁共振数据集上都取得了具有竞争力的性能,证明了其在现实场景中的鲁棒性和实用性。我们的工作有助于推进深度学习在分析化学和结构化学中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
11.70
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