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\section*{Results}  Consistent with previous studies \cite{Tiikkainen_2009}, we find the MUV dataset to be a challenging target for shape-based screening with few targets demonstrating AUCs far from random performance. Overall FOMS either matched or exceeded the virtual screening performance of VAMS while retaining most of the benefits of pre-aligned molecules. Specifically, it is orders of magnitude faster than the optimized optimizing  alignment of RDKit. RDKit suggesting, at a minimum, that FOMS is a viable method for rapidly pre-screening large libraries.  General Observations:  Protein targets with ligands that have many key interactions for their binding mode did not perform as favorably as targets with ligands that have a few key interactions. Targets with relatively few interactions may have the important features for protein inhibition covered by the SMARTS expression and fragment pre-alignment. These targets may easily reject compounds that did not match the SMARTS expression key for understanding the mode of inhibition.   In general, FOMS performed better than VAMS for every protein target. This indicates that it is an effective pre-screen, when considering the earlier success of VAMS (cite VAMS). In general, more specific SMARTS expressions led to a higher performance for both FOMS and VAMS. Higher specificity of fragment choice will lead to identifying singly the most important interactions involved in the binding mode of the ligand. In general, the interaction point method for both FOMS and VAMS yielded the highest performance (cite table). The interaction point method is able to include specific areas where interactions are likely to occur between the receptor and ligand. Logically, this approach should yield the highest performance since it uses the most information involved in ligand-receptor interactions.