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Metabolomics in the era of artificial intelligence

Elizabeth A Coler

Elizabeth A Coler Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA

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Wanxuan Chen

Wanxuan Chen Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA

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Alexey V Melnik

Alexey V Melnik Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
Arome Science Inc., Farmington, Connecticut, USA

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James T Morton

James T Morton Gutz Analytics LLC, Boulder, Colorado, USA

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Alexander A Aksenov

Alexander A Aksenov Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
Arome Science Inc., Farmington, Connecticut, USA

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Artificial intelligence (AI) is rapidly revolutionizing our daily lives, as it automates mundane tasks, enhances productivity, and transforms how we interact with technology. We believe it is inevitable that AI will soon become a crucial tool in common research practices, from data analysis to writing papers. Here we explore how this transition is occurring in the field of mass spectrometry-based metabolomics, a rapidly growing area of science. Metabolomics focuses on studying small molecules in biological systems, offering valuable insights into metabolic processes and their impact on health, diseases, and physiological conditions. With the remarkable advancements in sequencing technologies and the exploration of the microbiome, the combination of sequencing and metabolomics presents profound opportunities to understand biological complexity. Incorporating AI is promising to unlock new possibilities for expanding the realms of scientific discoveries. In this review we specifically focus on the current trends in the application of AI in metabolomics research. Existing practices are examined and a perspective on future directions for integrating AI into metabolomics research is presented.