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A major advantage of fragment alignment is that it enables the use of shape constraints.  Shape constraint search generally tracked or improved upon the performance of FOMS similarity ranking (e.g. Figure~\ref{cathg}). As shown in Table~\ref{pvaltable}, shape constraints were able to generate statistically significant ($p < 0.01$) enriched subsets for six of the ten targets.  Unlike whole-molecule shape similarity, shape constraints can select for a \textit{subshape} of the query ligand (i.e., perform partial shape matching \cite{Tangelder_2007}) and specifically filter out potential clashes with the receptor. Furthermore, shape constraints support an indexing search algorithm that scales sub-linearly. In our evaluation, shape constraint searches were orders of magnitude faster than even the FOMS similarity search and generally took well under a second.   Shape constraints provide a novel and unique method for specifying molecular shape queries. Although we have investigated automatically generated shape constraints, our assumption is that intelligently designed constraints created by human experts will substantially outperform our interaction point based constraints. The speed of shape constraint searches enables interactive analysis and refinement of shape queries where the expert user sculpts their desired molecule guided by the results of searches of full-sized databases. We anticipate that fragment-oriented shape constraint will prove a useful tool in both fragment-based drug discovery workflows, which are naturally centered around a fragment, and lead optimization workflows, where the core scaffold of the lead compound can serve as a fragment.  Shape constraints provide a novel and unique method for specifying molecular shape queries. Since they are fragment oriented, they can be used to perform partial shape similarity search, a generally challenging problem (cite). Shape constraint searches generally took well under a second making them perfect for interactive applications. Although we have investigated automatically generated shape constraints, our assumption is that intelligently designed constraints created by human experts guided by interactive analysis of a virtual screening database would substantially outperform our interaction point based constraints. This hypothesis remains untested as intuitive interfaces that allow expert users to sculpt their desired molecule need to be developed and integrated into a comprehensive virtual screening evaluation environment.