Water engages in two important types of interactions near biomolecules: it forms ordered cages around exposed hydrophobic regions, and it participates in hydrogen bonds with surface polar groups. Both types of interaction are critical to biomolecular structure and function, but explicitly including an appropriate number of solvent molecules makes many applications computationally intractable. A number of implicit solvent models have been developed to address this problem, many of which treat these two solvation effects separately. Here, we describe a new model to capture polar solvation effects, called SHO (solvent hydrogen-bond occlusion); our model aims to directly evaluate the energetic penalty associated with displacing discrete first-shell water molecules near each solute polar group. We have incorporated SHO into the Rosetta energy function, and find that scoring protein structures with SHO provides superior performance in loop modeling, virtual screening, and protein structure prediction benchmarks. These improvements stem from the fact that SHO accurately identifies and penalizes polar groups that do not participate in hydrogen bonds, either with solvent or with other solute atoms (unsatisfied polar groups). We expect that in future, SHO will enable higher-resolution predictions for a variety of molecular modeling applications. (c) 2017 Wiley Periodicals, Inc.