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<p>Although NMR spectroscopy is one of the most fundamental methods for structural characterization in organic chemistry, high-quality and large-scale experimental NMR datasets remain a critical bottleneck for training robust, generalizable spectral-prediction and structure-elucidation models. Here, we introduce NMRexp, an open, curated collection of 3.3 million experimental NMR records for six nuclei (<sup>1</sup>H, <sup>13</sup>C, <sup>19</sup>F, <sup>31</sup>P, <sup>29</sup>Si, <sup>11</sup>B), extracted from nearly 200 thousand Supporting Information documents published between 2010 and 2024. Manual evaluation confirmed &gt; 99% accuracy in metadata extraction (chemical shifts, multiplicities, <i>J</i>-couplings, solvents) and 98% correctness in molecular-skeleton assignment; internal consistency tests yielded mean absolute errors of 0.026 ppm for <sup>1</sup>H and 0.206 ppm for <sup>13</sup>C chemical shifts. NMRexp surpasses existing public NMR databases by over an order of magnitude in size, while providing rich spectral annotations and source-DOI traceability. We anticipate that NMRexp will accelerate the development of next-generation data-driven spectral prediction, automated structure elucidation, and related AI-enabled applications.</p>

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NMRexp: A database of 3.3 million experimental NMR spectra

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月18日

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  • Jun-Jie Wang,
  • Yongqi Jin,
  • Chen-Yu Zhi,
  • Yu-Jie Liu,
  • Xu-Hao Huang,
  • Fanjie Xu,
  • Xiaohong Ji,
  • Xi Fang,
  • Haoyi Tao,
  • Weinan E,
  • Linfeng Zhang,
  • Guolin Ke,
  • Rong Zhu

摘要

Although NMR spectroscopy is one of the most fundamental methods for structural characterization in organic chemistry, high-quality and large-scale experimental NMR datasets remain a critical bottleneck for training robust, generalizable spectral-prediction and structure-elucidation models. Here, we introduce NMRexp, an open, curated collection of 3.3 million experimental NMR records for six nuclei (1H, 13C, 19F, 31P, 29Si, 11B), extracted from nearly 200 thousand Supporting Information documents published between 2010 and 2024. Manual evaluation confirmed > 99% accuracy in metadata extraction (chemical shifts, multiplicities, J-couplings, solvents) and 98% correctness in molecular-skeleton assignment; internal consistency tests yielded mean absolute errors of 0.026 ppm for 1H and 0.206 ppm for 13C chemical shifts. NMRexp surpasses existing public NMR databases by over an order of magnitude in size, while providing rich spectral annotations and source-DOI traceability. We anticipate that NMRexp will accelerate the development of next-generation data-driven spectral prediction, automated structure elucidation, and related AI-enabled applications.