在化学信息提取中使用机器学习和大型语言模型
Yufan Chen, Yuxuan Zhang, Haifan Zhou
1Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China;
Annual review of chemical and biomolecular engineering
|February 23, 2026
概括
从科学文献中自动提取化学信息可以加速发现. 机器学习和大语言模型 (LLM) 的进步是化学科学数字化的关键.
科学领域:
- 化学信息学 化学信息学
- 计算化学是一种计算化学.
- 化学领域的数字化转型
背景情况:
- 化学文献包含了大量的数据,这些数据对于加速化学发现和优化至关重要.
- 手动将这些数据编制成结构化数据库是低效的,耗时的,昂贵的.
- 需要自动化方法来释放化学信息的潜力.
研究的目的:
- 从文献中审查自动提取化学信息的最新进展.
- 专注于利用图像和文本数据模式的方法.
- 引导研究人员应用机器学习和LLM技术.
主要方法:
- 研究了从基于规则和机器学习到大型语言模型 (LLM) 和视觉语言模型的演变.
- 专注于核心任务:光学化学结构识别,反应图解析,命名实体识别和实验过程提取.
- 突出了代表性方法,基准数据集和诸如多式联运集成和数据注释等挑战.
主要成果:
- 确定了当前自动化化学信息提取技术的关键趋势和局限性.
- 展示了向复杂的LLM和视觉语言模型应用程序的进展.
- 证明了多式联运一体化和数据注释策略日益重要.
结论:
- 自动化化学信息提取框架对于化学的数字化转型至关重要.
- 法律法学和视觉语言模型代表了最先进的技术,提供了显著的改进.
- 未来的工作应该专注于强大的,可扩展的和完全自动化的化学数据提取系统.
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