Computational Toxicology of Functional Foods and Herbal Products: Current Advances and Future Directions

Abstract

Computational toxicology, which integrates computational modelling, artificial intelligence (AI), and systems biology, is a powerful tool for improving the safety assessment of functional foods and herbal medicines. The review focuses on recent advances in computational tools such as quantitative structure–activity relationship (QSAR) modeling, molecular docking, molecular dynamics simulation, read-across methods, and prediction of ADMET and network toxicology for predicting the toxicological profile of bioactive compounds. The role of machine learning, deep learning, explainable AI, and predictive toxicology platforms in enhancing the accuracy of toxicity prediction is discussed. In addition, the review emphasizes the use of multi-omics approaches such as toxicogenomics, proteomics, metabolomics, and integrated systems toxicology to gain insights into mechanisms of toxicity. The current regulatory perspectives, such as OECD, FDA and EFSA guidelines, are also discussed along with validation strategies for models. Finally, the existing challenges, future research directions, and the potential of AI-driven precision toxicology are discussed. It is envisioned that computational toxicology will be a game-changer in the rate of risk assessment, minimizing animal testing, improving the science of regulatory decision-making, and providing a scientific blueprint for safe and effective functional foods and herbal therapeutics.