Artificial Intelligence in Mass Spectrometry-Based Metabolomics and Lipidomics for Drug Discovery and Development

Artificial intelligence applied to mass spectrometry-based metabolomics and lipidomics has advanced through three technological generations: chemometric and classical machine learning, deep learning, and self-supervised foundation models, with large language models so far entering mainly at the edges. The metabolome and lipidome lie closest to drug action, yet they have been among the slowest omics layers to adopt modern artificial intelligence. This paradox arises from their downstream position, which leaves them without a closed molecular reference and without the standardized, readily modeled data available to the genome and transcriptome. This review examines what these methods contribute to drug discovery and development. We survey applications across the drug-development pipeline, from target identification and mechanism-of-action deconvolution, through the prediction of drug-induced toxicity, to pharmacometabolomics, in which pre-dose metabolic phenotypes inform patient stratification and dosing. Across these metabolomic and lipidomic applications, interpretable classical machine learning remains the dominant form of artificial intelligence. The newer deep-learning and foundation models have concentrated instead on compound annotation, the cross-cutting problem of assigning confident identities to spectra, yet successive generations have mitigated but not solved it. Beyond annotation confidence, batch effects, semi-quantification, and small-cohort overfitting also remain practical constraints. Overall, targeted, hypothesis-driven metabolomic and lipidomic studies can already support drug discovery when their inferences are restricted to confidently identified metabolites, whereas open, hypothesis-free discovery remains the frontier. Near-term progress will depend less on any single algorithm than on curated spectral resources, rigorous measurement, leakage-aware validation, and honest reporting of identification confidence.