Application of quantitative structure-activity relationship to food-derived peptides: Methods, situations, challenges and prospects

Food-derived bioactive peptides have attracted extensive attention because of their antioxidant, antibacterial, antitumor and antihypertensive effects. The conventional approaches used to acquire bioactive peptides require complicated procedures involving enzymolysis, separation and identification. So far, data-driven computing methods have become an important tool for the screening, design and mechanism exploration of bioactive peptides. The quantitative structure-activity relationship (QSAR), a quantitative method used to describe the structure-activity relationship of compounds, has been widely used in drug design, material science, and chemistry; however, there are limited applications in food science. Here, we mainly focus on technologies used to perform QSAR modeling in peptides, including dataset collection, structural characterization, variable selection, correlation methods, and model validation and evaluation. We also summarize the recent applications, situations, challenges and prospects of the use of QSAR in food-derived bioactive peptides. Researchers should make full use of the benefits of QSAR as well as face its challenges. Multiple new methods or combination strategies should be used to achieve QSAR analysis. Much research is needed to improve the knowledge of QSAR in order to discover bioactive peptides. The solution to this task requires multidisciplinary cooperation in multiple fields, including chemistry, computer science, mathematics and, of course, food science.