Identification of metabolic vulnerabilities and synthetic lethal targets utilizing artificial intelligence-driven precision medicine

Cancer metabolism is highly plastic, and most attempts to turn that plasticity into a therapeutic target fail for a specific, recurring reason: a computational prediction of a metabolic vulnerability is not the same evidentiary object as an experimentally confirmed one, and the two are routinely discussed as if they were interchangeable. This review takes that distinction as its starting point rather than its conclusion. Artificial intelligence and genome-scale systems biology have made it easy to generate metabolic hypotheses at a scale no experimental program could match — machine learning models trained on genomic, transcriptomic, proteomic, and metabolomic data now output candidate vulnerabilities, biomarkers, and drug combinations faster than they can be tested — but the resulting literature has not been consistently honest about how far any given prediction has actually traveled toward clinical relevance. We examine six areas where this gap is most visible: multi-omics data integration, genome-scale metabolic network modeling, computational discovery of metabolic vulnerabilities and synthetic lethal interactions, single-cell and spatial resolution of metabolic heterogeneity, AI-guided drug and biomarker discovery, and the small set of studies that have actually reached clinical testing. Rather than summarizing these areas as separate literatures, we grade every substantive finding discussed against an explicit four-tier evidence standard — associative, mechanistic, perturbation-validated, or clinically tested — and we apply that standard critically rather than deferentially: several widely cited computational vulnerabilities remain unconfirmed outside the cell lines in which they were discovered, genome-scale models continue to carry unresolved gaps in reaction coverage that limit their predictive reach, and, of the AI-derived metabolic predictions discussed in this review, only one has so far been tested in a completed prospective clinical trial. We argue that this shortage of prospective validation, not a shortage of computational sophistication, is the field’s central constraint, and we close by identifying where methodological effort would do the most good: standardized validation reporting, tissue-resolved rather than bulk metabolic models, and closer coupling between computational prediction and the specific experimental designs capable of falsifying it.