Artificial intelligence in metabolomics: a current review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health. •We provide a recent overview of the methodologies and applications of artificial intelligence (AI) in metabolomics.•We introduce metabolomics and AI algorithms that are commonly used in big data analyses.•Recent applications of AI-assisted metabolomics in the context of systems biology and human health are summarized.•Challenges and future perspectives for the development and applications of AI approaches in metabolomics are discussed.