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Protein-related interaction prediction is critical to understanding life processes, biological
functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated
interactions have always been costly and inefficient. In recent years, advances in biological
and medical technology have provided us with explosive biological and physiological data, and
deep learning-based algorithms have shown great promise in extracting features and learning patterns
from complex data. At present, deep learning in protein research has emerged. In this review,
we provide an introductory overview of the deep neural network theory and its unique properties.
Mainly focused on the application of this technology in protein-related interactions prediction over
the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein–
drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning
currently faces.
Learning Patterns from COVID-19 Instances
