Food-derived phenolic compounds in precision nutrition: computational and AI-assisted approaches for target...

Precision nutrition refers to nutritional interventions tailored to individual biological variability (e.g., genetics, gut microbiome, and metabolic status). Within this framework, identifying bioactive food components that modulate disease-relevant targets and pathways in an individualized manner is a central goal. Targeted interventions are increasingly used for cancer, autoimmune disorders, and metabolic diseases, but are often limited by high prices and adverse effects. Dietary phenolic compounds have therefore attracted attention as safer and more sustainable candidates for health intervention strategies, owing to their ability to interact with disease-relevant protein targets. Examples include EGCG targeting the p53-MDM2 interaction, quercetin modulating HSP90-related ferroptosis, and sesamin inhibiting Syk activation in food allergy. Their structural diversity and bioactivity make them valuable for candidate screening and personalized intervention design. However, these features also create challenges for target identification and functional evaluation. More systematic and predictive strategies are therefore needed to accelerate the identification, evaluation, and optimization of phenolic bioactives. In this context, computational and AI-assisted approaches (e.g., molecular docking, machine learning, and network pharmacology) offer new opportunities to improve target discovery, candidate prioritization, and response prediction. These approaches can integrate individual-level genetic, microbiome, metabolic, dietary, and clinical data. This integration may help predict personalized molecular targets, phenolic metabolism, bioavailability, and intervention responses, thereby supporting tailored phenolic-based nutrition strategies. This review summarizes the therapeutic potential, computational discovery strategies, molecular targets, and translational gaps in applying phenolic compounds to precision nutrition.