AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological sam-ples. One utility of untargeted metabolomics, unbiased global anal-ysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics work-flows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metab-olites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identifi-cation in untargeted metabolomics and exposomics studies.

Automated summary

This article discusses the application of artificial intelligence (AI) and machine learning (ML) in untargeted metabolomics and exposomics, and their significant findings in disease screening and diagnosis. It introduces the principles of metabolomics and exposomics and explores the potential opportunities to improve data quality and chemical identification using AI and ML.