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\subsection{ER$\alpha$}  ER-a with its best approach Virtual screening results for estrogen receptor $\alpha$ agonists  and SMARTS expression did not perform significantly better than random inhibitors are shown in Figures~\ref{eralphapot} and \ref{eralpha}. All three shape similarity methods exhibited poor  performance for with this query, but many of  the FOMS or VAMS method (cite table). Although FOMS was interaction point queries were  significantly betterperforming  than VAMS, it still did not perform well enough for ER-a agonists to be considered an effective screening method.   Similar to ER-a, the random. The statistically most significant  interaction with Glu353 and Arg394 point query  is vital shown in Figure~\ref{cathgip}. The fragment  foragonism of ER-a (cite crystal ER-a). There is also an indication that  the stereochemistry query  is an important consideration for ER-a agonism when trying to avoid steric clash with W383, L536, deeply buried within the S1 pocket  andL539. The study reporting  the crystal structure of interaction points match  the ER-a agonist cites, “…dihydrobenzoxathiin SERAMs [are] highly dependent on size and location of side chain substituents” (cite part 9). For this reason, it makes sense that our SMARTS expressions S2 pocket  and fragment pre-alignments did not perform significantly better than random chance (cite table).   Docking phosphonate moiety  of1XP9 and its active set from MUV was performed using smina, and  the two main interactions observed were with Glu353 and Arg394 concurrently (visual inspection). This indicates that both residues are important interactions for an ER-a agonist. In addition, ligand. However,  there were 5 pockets available for filling. Three of these pockets were occupied by almost every conformer of the active compounds, with one of are no interaction points selected in  the main pockets being covered by fragment pre-alignment. The variation S3/S4 pocket. Interactions  in which pockets needed to be filled indicates that there this pocket can increase affinity, but  are not necessary for binding \cite{de_Garavilla_2005}. Unlike whole-ligand similarity methods, shape constraint search can ignore  a plethora significant part  ofhydrophobic interactions that need to occur for ER-a agonism. This could explain  the poor performance geometry  of both methods since it the ligand (the naphthalene in the S3 pocket) and select for  only pre-aligned to one specific parts  of the many pockets.   For ER-a agonists, more specific SMARTS expressions had query shape. In this case, this selectivity results in substantially  better performance for FOMS and VAMS (cite table). The best performing SMARTS expression covered the interaction with Glu353 and Arg394. This virtual screening results that  iswhat most likely gave it the best performance since interaction with these  two residues are vital for ER-a agonism. (explanation for VAMS performance) orders of magnitude faster than they pre-aligned similarity search methods.