Spectrum–structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum–structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure–spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum–structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum–structure correlation soon, which would trigger substantial development of various disciplines.

中文翻译：

---

深度学习辅助的谱-结构相关性：最新技术和前景


谱-结构相关性在谱分析中发挥着越来越重要的作用，并在近几十年来取得了显着的发展。随着光谱仪的进步，高通量检测引发光谱数据的爆发式增长，从小分子到生物分子的研究延伸伴随着海量的化学空间。面对光谱-结构相关性不断发展的前景，传统的化学计量学变得力不从心，而深度学习辅助化学计量学迅速成为一种蓬勃发展的方法，具有提取潜在特征和做出精确预测的卓越能力。在这篇综述中，首先介绍了深度学习的分子和光谱表示以及基础知识。然后，我们总结了近5年来深度学习如何通过增强光谱预测（即正向结构-光谱相关性）以及进一步实现库匹配和从头分子生成（即从头生成）来帮助建立光谱和分子结构之间的相关性的发展。 ，逆谱-结构相关性）。最后，我们强调了持续存在的最重要的未解决问题以及相应的潜在解决方案。随着深度学习的快速发展，建立谱结构相关性的最终解决方案有望很快出现，从而引发各学科的长足发展。