The goal of this research proposal is to improve the ability of the SMoG potential to correctly rank the binding affinities of inhibitors that take advantage of binding modes mediated by electrostatic interactions. The SMoG potential was developed in order to predict binding affinities with the long-term goal of being the backbone of an efficient drug design protocol. The potential itself is a fully atomic knowledge-based function fit to a database of protein-ligand complexes. This trained potential has been demonstrated to quickly rank the relative binding energies of a large number of ligands. It has also been observed, however, that the predictive ability of the SMoG potential appears to be inversely related to the polarity of the ligand. Using two distinct approaches, I hope to make the SMoG potential more robust with regard to polar inhibitors and binding sites.