This chapter summarizes the recent development of computational approaches in the formulation of biopharmaceuticals, particularly focusing on proteins, ribonucleic acid (RNA), and cell therapies. The complexity of biologics, which include proteins, nucleic acids, or cells, demands precise formulation strategies to ensure stability, efficacy, and safety during storage, transport, and administration. Traditional experimental approaches are often costly and time-consuming, leading to a growing interest in computational methods that can streamline the formulation process, predict stability issues, and enhance the understanding of molecular interactions. For protein formulations, computational methods like machine learning and molecular simulations are employed to select and optimize excipients, predict high-concentration antibody aggregation and viscosity, and detect subvisible particles. In RNA formulations, lipid nanoparticles play a crucial role in delivering mRNA vaccines. Computational modeling, including machine learning and molecular dynamics simulations, aids in optimizing these nanoparticles for better encapsulation efficiency, stability, and delivery. These methods provide insights into lipid-mRNA interactions and help design effective lipid nanoparticle systems. Cell therapy formulations benefit from machine learning approaches that optimize culture media. By integrating machine learning with the design of experiments, researchers can develop effective media formulations in a single experimental step, enhancing cell viability and expansion across different donors. Automation of biologic formulation can also enhance model development. Overall, integrating computational methods into biopharmaceutical formulation offers a robust framework for developing stable, effective, and safe biologics, significantly reducing time and resource expenditures in the development process.