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Simulation of small molecules, polymers, and proteins in dense solvents is an important class of problems both for processing the materials in liquids and for simulation of proteins in physiologically relevant solvent states. However, these simulations are expensive and sampling is inefficient due to the ubiquitous dense solvent. Even in the absence of the dense solvent, rigorous sampling of the configurational space of chain molecules and polypeptides with traditional Metropolis Monte-Carlo, or molecular dynamics is difficult due to long time scales associated with equilibration. In this paper we discuss a series of configurational-bias Monte-Carlo (CBMC) simulations that use a rigorous molecular theory based implicit solvent to achieve an efficient sampling of a chain molecule in a dense liquid solvent. The molecular theory captures solvent packing around the chain molecule as well as the energetic effects of solvent-polymer interactions. It also accounts for entropic effects in the solvent

A combined molecular simulation-molecular theory method applied to a polyatomic molecule in a dense solvent