Computational Prediction of Bioavailability and Bioactivity of Functional Food Components

Abstract

Computational methods have revolutionized the research of functional food by allowing the prediction of bioavailability and bioactivity of bioactive food compounds in a few seconds. The chapter emphasizes the use of advanced computational methods such as quantitative structure–activity relationship (QSAR) modeling, molecular docking, molecular dynamics simulation, absorption, distribution, metabolism, excretion (ADMET) prediction, physiologically based pharmacokinetic (PBPK) modeling, and artificial intelligence (AI) for the rapid assessment of functional food ingredients. All these techniques help in rapid identification of potential bioactive molecules with better pharmacokinetic properties and therapeutic potential with reduced time and cost of experimentation. AI-based applications in disease prevention, gut microbiome modulation, precision nutrition using machine learning, deep learning, network pharmacology, and multi-omics integration are also discussed. Topics such as data standardization, experimental validation, regulatory aspects, and explainable AI are also covered. In summary, computational prediction offers a comprehensive framework for creating evidence-based functional foods and nutraceuticals, enabling personalized nutrition and driving future advancements in healthcare and nutrition through data-driven innovation.