Hydrophobic aggregation is a ubiquitous process in chemistry and biology that leads to important phenomena such as protein folding and assembly. Given an accurate Hamiltonian of a system that undergoes these phenomena, computational sampling techniques can be utilized to determine properties such as structure of the native state and folding kinetics. The standard Hamiltonian used to sample these systems contains the degrees of freedom of all solute and solvent particles and is referred to explicit solvent all-atom molecular dynamics. Unfortunately, protein folding and assembly happen at time- and length-scales that are unapproachable using this conventional technique. One way to overcome this limitation is to build the physics of the solvent into the Hamiltonian of the system. In this talk, I will explore the currently available versions of this technique, referred to as implicit solvation, and present a new approach that accurately captures hydrophobic aggregation. Our model, named IS-SPA, is specifically designed to capture the dimerization thermodynamics and further aggregation of hydrophobic solutes in water. We further develop the IS-SPA sampling method to accurately capture the dynamics of hydrophobic aggregation. Combined, IS-SPA provides an appealing approach to accurately capture the dynamics and thermodynamics of hydrophobic aggregation in large-scale processes such as hydrophobic collapse.