Computational approaches to predict the toxicity of bioactive natural products: a mini review of methodologies

• Published in: Food science and biotechnology Vol. 34; no. 2; pp. 299 - 305
• Main Authors: Choi, Kwanyong, Kim, Ji Yeon
• Format: Journal Article
• Language: English
• Published: Korea (South) Springer Nature B.V 01.01.2025; 한국식품과학회
• Subjects: Biocompatibility; Biological activity; Biotechnology; Composition effects; computer simulation; Food composition; Food safety; Food science; Food selection; Functional foods & nutraceuticals; Machine learning; Natural & organic foods; Natural products; Physiological effects; Predictions; Side effects; Toxicity; Toxicity testing; 식품과학; In silico; Top-down; Natural products; Toxicity; Bottom-up
• ISSN: 1226-7708; 2092-6456; 2092-6456
• DOI: 10.1007/s10068-024-01701-1

Despite the increasing global demand for functional foods, the challenges associated with bioactive natural food products due to their complex composition remain. Bioactive natural products can potentially interfere with physiological activity regulation and lead to undesired side effects. This finding emphasizes the need for machine learning (ML)-based food safety predictions focused on intrinsic toxicity. This review explores various strategies involved in current methods of model selection and validation techniques used in predictive analysis, highlighting their strengths, limitations, and progress. Future studies should focus on testing compound combinations using top-down or bottom-up approaches with appropriate models to advance in silico toxicity modeling of bioactive natural products.