Researchers have developed models to predict protein-protein interactions, which may lead to new insights into disease and drug discovery. These models, which include language models and deep learning approaches, aim to improve the understanding of protein interactions. Various studies have proposed different methods, including physics-informed geometric invariant learning and parameter-efficient fine-tuning, to advance protein-protein interaction modeling.