Machine Learning for Peptide Structure, Function, and Design

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Background

Peptides, which have a short sequence from 2 to 50 amino acids, play an important role in the biological process. In recent years, a large number of functional peptides have been identified with a wide range of biological properties such as anticancer peptides (ACPs), antiviral peptides, antimicrobial peptides, etc. Furthermore, peptide-based therapy has become a promising way to treat various diseases.

However, it is usually laborious, time-consuming, and costly to obtain the function of various peptides with experimental methods. Nowadays, machine learning technologies have been widely applied in many biomedical research areas, providing an abundance of knowledge, such as protein structure prediction, function prediction, and so on. With the emergence of new technologies and methods in machine learning, especially in deep learning, there are many opportunities for researchers to develop novel methods and tools to better understand various peptides and design highly precise drug peptides.

This Research Topic focuses on the new methods and applications of machine learning algorithms to promote the understanding of various peptides in biomedical science. This collection includes but is not limited to:
1) Prediction of secondary and tertiary structures of peptides
2) Unbalanced samples analysis in peptide datasets
3) Prediction of peptide function based on machine learning methods
4) Identification of therapeutic peptides from next-generation sequencing
5) Prediction of peptides with specific functions such as anticancer peptides, antiviral peptides, antimicrobial peptides, etc.
6) Computer-aided prediction and design of synthetic drug delivery peptides

Keywords: Machine learning, Functional peptides, Deep learning, Drug design, Peptide therapeutics

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