# AI redefine untargeted metabolomics: estimating chemical amounts for a Human Exposome Project

## TL;DR

This paper explores how AI can help quantify chemicals in untargeted metabolomics, advancing the Human Exposome Project by linking chemical exposures to health outcomes.

## Contribution

The paper introduces AI-driven methods for estimating chemical concentrations without authentic standards, enabling semi-quantitative metabolomics.

## Key findings

- AI models can predict ionization response factors for metabolites lacking reference standards.
- Matrix-embedded calibration using endogenous metabolites improves normalization across studies.
- Tiered semi-quantification allows classification of exposures into biologically relevant concentration ranges.

## Abstract

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.