From AI Prediction to Experimental Validation of Food-Derived Bioactives: A Critical Review

Foods contain a large, only partially characterized chemical space, and artificial intelligence (AI) is increasingly used to prioritize candidate bioactives. This review critically examines AI-driven discovery of functional food ingredients and bioactive compounds across the discovery-to-translation continuum. We discuss the data landscape supporting computational discovery, including food composition, omics, and microbiome resources, together with challenges in annotation, standardization, and integration. Major computational approaches, including machine learning, quantitative structure–activity relationship modeling, deep learning, molecular docking, natural language processing, knowledge graphs, and generative design, are reviewed alongside biotechnological platforms for candidate generation and production. Applications in cardiometabolic health, oxidative stress and inflammation, and gut health are considered. Particular emphasis is placed on a structured validation pathway encompassing chemical confirmation, biological activity, gastrointestinal stability, food-matrix bioaccessibility, intestinal transport, microbiota-mediated transformation, animal and human evidence, safety, and technological feasibility. Current AI-assisted discoveries remain concentrated at early validation stages; predicted bioactivity should therefore be treated as a hypothesis rather than evidence of functional efficacy. Finally, we outline methodological, ethical, regulatory, and data requirements for closed-loop discovery frameworks integrating computational prediction with experimental validation.