Xue Xi, Sun Hanyu, Yang Minjian, Liu Xue, Hu Hai-Yu, Deng Yafeng, Wang Xiaojian
State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China.
Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China.
Anal Chem. 2023 Sep 19;95(37):13733-13745. doi: 10.1021/acs.analchem.3c02540. Epub 2023 Sep 9.
The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet-visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors' personal outlook on the development and future applications of spectral interpretation.
光谱数据的解读，包括质谱、核磁共振谱、红外光谱和紫外可见光谱，对于获取分子结构信息至关重要。先进传感技术的发展使可用光谱数据量成倍增加。化学专家必须运用与分子碎片和官能团产生的光谱信息相对应的基本原理。这是一个耗时的过程，需要坚实的专业知识基础。近年来，计算机科学的快速发展及其在化学信息学中的应用以及计算机辅助专家系统的出现，大大降低了分析大量数据的难度。然而，对于专家系统而言，解决问题的策略必须预先知晓或由人类专家提取并转化为算法。令人欣慰的是，人工智能（AI）方法的发展在解决此类问题方面显示出巨大潜力。传统算法，包括最新的神经网络算法，在提取有用信息和处理海量数据方面都显示出巨大潜力。本综述重点介绍了涵盖所有新兴的基于人工智能的光谱解读技术的最新创新成果。此外，还介绍了主要局限性和当前障碍，并提出了相应的进一步研究方向。此外，本综述给出了作者对光谱解读发展及未来应用的个人展望。
