Food-Derived Bioactive Peptides And Artificial Intelligence Techniques For Their Prediction: A Brief Review
[Full Text]
AUTHOR(S)
Margarita Terziyska, Ivelina Desseva, Zhelyazko Terziyski
KEYWORDS
artificial intelligence, activity prediction, food-derived peptides, deep learning, neural networks.
ABSTRACT
Biologically active peptides (BAPs) have a positive effect on human health, which is why they are used as a basis for drug and functional foods development. They are therefore of economic interest. However, the process of their isolation is too expensive and time-consuming. Hence, it is necessary to develop more effective methods to predict the potential activity of peptides. An appropriate solution could be an in silico approach, in particular the use of computational methods based on artificial intelligence (AI) techniques. The use of AI approaches may facilitate the identification of bioactive peptides. Thus, in this paper, along with some basic information about food-derived BAPs, a brief review of the AI techniques used for their activity prediction is made.
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