Computationally predicting the binding conformations of ligands to a receptor allows for the rapid screening of thousands of potential ligands to find the most viable lead compounds, an efficient precursor to traditional synthesis and biological assays . The compounds with the most negative binding energies (in kcal/mol) represent the most stable conformations; the lower the binding energy, the better a ligand can bind to the active site and is therefore predicted to be a more potent inhibitor. Here, we use these binding scores to identify lead compounds with the most potential. Additionally, catalogs of commercially available drug structures enable the targeted screening of compounds with known properties and metabolic activity. Especially with the availability of the Zinc database , we were able to preliminarily filter out small molecules with nonoptimal drug-likeness (an estimation of a compound's in-vivo potential based on its physiochemical properties) by selecting ligands with favorable molecular weight and lipophilicity-factors that have been demonstrated to impact molecular absorption, metabolism, and other factors (more details in methods/results) .