AI-Assisted Discovery of Food-Derived Bioactive Compounds for Personalized Nutrition

Abstract

The antioxidant, anti-inflammatory, antimicrobial, and immunomodulatory properties of food-derived bioactive compounds have given rise to their role as important contributors to disease prevention and health promotion. However, traditional techniques for identification and validation of these compounds can be time-consuming and labour-intensive. Under the hood, the field has seen a massive transformation by artificial intelligence (AI), through the use of machine learning, deep learning, and predictive analysis, which have enabled fast screening, bioactivity prediction, molecular interaction analysis, and even personalised dietary recommendations. Advances in the integration of nutrigenomics, gut microbiome profiling, metabolomics, and other multi-omics technologies have further progressed precision nutrition by providing the ability to design dietary interventions based on an individual's genetic, metabolic, and physiological makeup. The application of AI computational methods can shorten the process of functional food ingredients identification and improve their safety, efficacy, and clinical relevance. Despite the challenges of data integration, explainable AI, and regulatory standards, the potential for personalized nutrition and preventive healthcare through AI technology is promising. Overall, the use of AI in the discovery of bioactive compounds from food is a paradigm shift in the field of developing evidence-based nutrition interventions to help reduce the prevalence of chronic diseases and improve overall human health.