In untargeted metabolomics, structures of small molecules are annotated using liquid chromatography-mass spectrometry by leveraging information from the molecular retention time (RT) in the chromatogram and m/z (formerly called ''mass-to-charge ratio'') in the mass spectrum. However, correct identification of metabolites is challenging due to the vast array of small molecules. Therefore, various in silico tools for mass spectrometry peak alignment and compound prediction have been developed; however, the list of candidate compounds remains extensive. Accurate RT prediction is important to exclude false candidates and facilitate metabolite annotation. Recent advancements in artificial intelligence (AI) have led to significant breakthroughs in the use of deep learning models in various fields. Release of a large RT dataset has mitigated the bottlenecks limiting the application of deep learning models, thereby improving their application in RT prediction tasks. This review lists the databases that can be used to expand training datasets and concerns the issue about molecular representation inconsistencies in datasets. It also discusses the application of AI technology for RT prediction, particularly in the 5 years following the release of the METLIN small molecule RT dataset. This review provides a comprehensive overview of the AI applications used for RT prediction, highlighting the progress and remaining challenges. This article focuses on the advancements in small molecule retention time prediction in computational metabolomics over the past five years, with a particular emphasis on the application of AI technologies in this field. It reviews the publicly available datasets for small molecule retention time, the molecular representation methods, the AI algorithms applied in recent studies. Furthermore, it discusses the effectiveness of these models in assisting with the annotation of small molecule structures and the challenges that must be addressed to achieve practical applications.

中文翻译：

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使用深度学习预测液相色谱中小分子保留时间的见解


在非靶向代谢组学中，使用液相色谱-质谱法通过利用色谱图中的分子保留时间 （RT） 和质谱中的 m/z（以前称为“质荷比”）的信息来注释小分子的结构。然而，由于小分子种类繁多，正确鉴定代谢物具有挑战性。因此，已经开发了各种用于质谱峰对齐和化合物预测的计算机分析工具;然而，候选化合物的列表仍然很广泛。准确的 RT 预测对于排除错误的候选物和促进代谢物注释很重要。人工智能 （AI） 的最新进展导致深度学习模型在各个领域的使用取得了重大突破。大型 RT 数据集的发布缓解了限制深度学习模型应用的瓶颈，从而提高了它们在 RT 预测任务中的应用。本文列出了可用于扩展训练数据集的数据库，并关注数据集中分子表示不一致的问题。它还讨论了 AI 技术在 RT 预测中的应用，特别是在 METLIN 小分子 RT 数据集发布后的 5 年中。这篇综述全面概述了用于 RT 预测的 AI 应用，重点介绍了进展和仍然存在的挑战。本文重点介绍了过去五年计算代谢组学中小分子保留时间预测的进展，特别强调了人工智能技术在该领域的应用。 它回顾了公开可用的小分子保留时间数据集、分子表示方法、最近研究中应用的 AI 算法。此外，它还讨论了这些模型在协助注释小分子结构方面的有效性，以及实现实际应用必须解决的挑战。