Artificial intelligence and in silico discovery of senolytic and senescence-associated food peptides

Cellular senescence, characterized by the senescence-associated secretory phenotype, is a primary driver of age-related tissue dysfunction. While synthetic senolytics have shown promise, concerns regarding systemic toxicity have shifted focus toward sustainable alternatives like food-derived bioactive peptides. This review evaluates AI and in silico methods in senolytic food peptide research, distinguishing discovery, prioritization, and unverified senescence relevance. We evaluate discovery pipelines across soy, dairy, legumes, and marine sources, addressing pharmacokinetics, bioavailability, senescence heterogeneity, gastrointestinal stability, bitterness, and the lack of standardized replicative and therapy-induced experimental senescence models. We further highlight that a substantial proportion of peptides discussed in the current literature are best characterized as senescence-associated candidates that await mechanistic validation, rather than as confirmed senolytics or senomorphics. Future research must prioritize bioengineered 3D human tissue models and long-term clinical validation.