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NMRExtractor: leveraging large language models to construct an experimental NMR database from open-source scientific
Qinggong Wang1,2, Wei Zhang2,3, Mingan Chen2,4,5
1Nanjing University of Chinese Medicine 138 Xianlin Road Nanjing 210023 China myzheng@simm.ac.cn.
Chemical Science
|May 30, 2025
Summary
NMRExtractor automates nuclear magnetic resonance (NMR) data extraction from scientific literature. This tool created NMRBank, a large, open-access dataset of NMR information to accelerate chemical research.
Area of Science:
- Chemistry
- Computational Chemistry
- Data Science
Background:
- Nuclear magnetic resonance (NMR) spectroscopy is vital for determining molecular structures.
- Manual NMR data extraction from publications is a significant bottleneck in chemical research.
Purpose of the Study:
- To develop an automated tool, NMRExtractor, for efficient NMR data extraction.
- To create a comprehensive, open-access NMR dataset, NMRBank, from scientific literature.
Main Methods:
- A fine-tuned large language model was employed for data extraction.
- Processed over 5.7 million open-source scientific publications.
- Constructed NMRBank with 225,809 entries, including compound IUPAC names, NMR conditions, 1H and 13C NMR chemical shifts, confidence levels, and references.
Main Results:
- NMRBank exhibits a chemical space significantly larger than existing public NMR datasets.
- The NMRExtractor tool demonstrated high scalability for processing and updating the NMRBank dataset.
- Successfully automated the extraction of critical NMR data points.
Conclusions:
- NMRExtractor and NMRBank address the scarcity of accessible experimental NMR data.
- This work provides a foundation for AI-driven NMR predictions and advances chemical research.
- Automated data extraction and a continuously updated database accelerate progress in chemistry.
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