Sustainable Discovery of Natural Anti-Aging Bioactives from Food Resources: Current Status and Machine Learning Perspectives

Existing anti-aging drugs are often limited by toxicity and resistance. In contrast, natural substances derived from food resources, edible plants, and agricultural by-products offer advantages such as low toxicity and suitability for dietary intake. Utilizing these resources aligns with sustainable development goals by promoting the valorization of food waste and functional food development; however, their complex composition makes traditional discovery inefficient and resource-intensive. Machine learning (ML) provides a powerful, sustainable in silico solution. By analyzing vast datasets, computational models can rapidly screen thousands of candidates, significantly reducing the chemical waste and time associated with traditional wet-lab screening. This review focuses on the current status of food-derived anti-aging bioactives and the emerging ML-based perspectives in this field. Key natural compounds and plant extracts are discussed, highlighting their dietary origins and mechanisms. Furthermore, we explore how advanced algorithms accelerate the identification of novel bioactives. Importantly, we address current translational gaps, including the need for explainable AI, ADME (Absorption, Distribution, Metabolism, and Excretion) prediction, and the standardization of complex mixtures. Overcoming these bottlenecks is essential for the sustainable development of effective, food-based anti-aging ingredients.