Metabolomics involves the comprehensive analysis of small molecules within a biological sample. Typically acquired using mass spectrometry, metabolomics data reflect the cellular state which can provide insights into health, disease, environmental toxicity, industrial technology, and other areas. Metabolomics data is rich and complex, requiring specialized techniques to interpret the data. With improved detection technologies and advances in machine learning and generative AI, computational analysis of metabolomics is rapidly expanding. This Dagstuhl Seminar extends the Computational Metabolomics series by focusing on enhancing our understanding of metabolomics data and turning the data into actionable biological insights. During this seminar, we aim to discuss topics related to several current challenges in metabolomics.