AI redefine untargeted metabolomics

The Human Exposome Project aims to map the totality of environmental exposures, but its success relies on transforming qualitative detections into quantitative data. Following our review on AI-driven metabolite identification, this second installment addresses the next critical bottleneck: estimating chemical concentrations in untargeted metabolomics without authentic standards. Translating LC-HRMS signal intensities into absolute concentrations is hindered by the vast variability in ionization efficiency and matrix effects, particularly for xenobiotics where reference standards are unavailable. We review emerging strategies that leverage artificial intelligence—ranging from descriptor-based regression to deep learning on molecular point clouds—to predict ionization response factors. We further evaluate a “matrix-embedded” calibration approach that utilizes ubiquitous endogenous metabolites (e.g., amino acids, lipids) as internal anchors to normalize response scales across studies. These innovations enable “tiered semi-quantification,” allowing the classification of exposures into biologically relevant ranges (e.g., nanomolar vs. micromolar). This stratification facilitates direct integration with toxicological frameworks, such as the Threshold of Toxicological Concern (TTC) and high-throughput bioactivity data (e.g., ToxCast), for rapid risk prioritization. By integrating quantitative AI prediction models with robust quality assurance, untargeted metabolomics can evolve from a qualitative discovery tool into a quantitative engine for exposure science, providing the necessary evidence to link complex chemical exposures to human health outcomes

Chemical safety · Complex matrix · Exposome · Human health · Metabolomics · Risk assessment · Computational Drug Discovery Methods · Health, Environment, Cognitive Aging · Metabolomics and Mass Spectrometry Studies

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