Computational approaches to predict protein–protein interactions in crowded cellular environments are reviewed, highlighting the importance of considering molecular crowding in understanding protein behavior. The cellular environment is highly crowded, with up to 40% of the cytoplasmic volume occupied by biomolecules, significantly affecting protein interactions. While experimental and computational methods have provided insights into protein interactions, they often overlook the impact of crowding. This review discusses theoretical and computational approaches that model biological systems to guide experiments and advance the study of protein interactions in crowded environments. Topics include statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion in high-viscosity environments, and molecular dynamics simulations. The review emphasizes the need for integrating computational and experimental methods to better understand the effects of crowding on protein interactions. It also discusses the challenges of studying crowding, such as the difficulty in replicating cellular conditions in vitro and the limitations of experimental techniques. The review highlights the importance of considering crowding effects on protein structure, dynamics, and binding, and the potential of computational methods to complement experimental studies. The review also addresses the role of soft interactions and hard-core repulsion in protein interactions and the impact of crowding on protein folding, stability, and binding affinity. The review concludes that computational methods, including molecular dynamics simulations and machine learning approaches, are essential for predicting protein interactions in crowded environments and understanding their biological implications.Computational approaches to predict protein–protein interactions in crowded cellular environments are reviewed, highlighting the importance of considering molecular crowding in understanding protein behavior. The cellular environment is highly crowded, with up to 40% of the cytoplasmic volume occupied by biomolecules, significantly affecting protein interactions. While experimental and computational methods have provided insights into protein interactions, they often overlook the impact of crowding. This review discusses theoretical and computational approaches that model biological systems to guide experiments and advance the study of protein interactions in crowded environments. Topics include statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion in high-viscosity environments, and molecular dynamics simulations. The review emphasizes the need for integrating computational and experimental methods to better understand the effects of crowding on protein interactions. It also discusses the challenges of studying crowding, such as the difficulty in replicating cellular conditions in vitro and the limitations of experimental techniques. The review highlights the importance of considering crowding effects on protein structure, dynamics, and binding, and the potential of computational methods to complement experimental studies. The review also addresses the role of soft interactions and hard-core repulsion in protein interactions and the impact of crowding on protein folding, stability, and binding affinity. The review concludes that computational methods, including molecular dynamics simulations and machine learning approaches, are essential for predicting protein interactions in crowded environments and understanding their biological implications.