  Peptides play a pivotal role in a wide range of biological activities through
participating in up to 40% protein-protein interactions in cellular processes.
They also demonstrate remarkable specificity and efficacy, making them
promising candidates for drug development. However, predicting peptide-protein
complexes by traditional computational approaches, such as Docking and
Molecular Dynamics simulations, still remains a challenge due to high
computational cost, flexible nature of peptides, and limited structural
information of peptide-protein complexes. In recent years, the surge of
available biological data has given rise to the development of an increasing
number of machine learning models for predicting peptide-protein interactions.
These models offer efficient solutions to address the challenges associated
with traditional computational approaches. Furthermore, they offer enhanced
accuracy, robustness, and interpretability in their predictive outcomes. This
review presents a comprehensive overview of machine learning and deep learning
models that have emerged in recent years for the prediction of peptide-protein
interactions.
