In Silico Drug Discovery Using Functional Food Bioactive Compounds

Abstract

Functional foods produce bioactive compounds with several pharmacological properties and safety characteristics, which can be used as excellent candidates in the development of new therapeutic products. With the advent of new in silico drug discovery technologies, computational methods including artificial intelligence, molecular docking, virtual screening, molecular dynamics simulation, ADMET prediction, and others have significantly contributed to the identification, screening and optimization of these natural compounds. These techniques can be used to rapidly profile protein–ligand interaction, pharmacokinetic characteristics, and toxicity and can significantly reduce the time and expense required to the traditional drug discovery process. Moreover, the combination of machine learning, network pharmacology and multi-omics approaches has improved the discovery of targets and lead optimization in precision medicine. The therapeutic potential of bioactive compounds of functional foods has been reported against cancer, cardiovascular diseases, diabetes, neurodegenerative diseases, infectious and inflammatory diseases. Overall, computational approaches provide a rapid, robust platform to improve the functional food-based drug discovery process, and to develop safe, effective and personalized therapeutic agents.