deletions | additions       diff --git a/subsection_ER_alpha_Virtual_screening__.tex b/subsection_ER_alpha_Virtual_screening__.tex  index 3e788ea..b249791 100644  --- a/subsection_ER_alpha_Virtual_screening__.tex  +++ b/subsection_ER_alpha_Virtual_screening__.tex 
...
\subsection{ER$\alpha$}  Virtual screening results for estrogen receptor $\alpha$ agonists and inhibitors are shown in Figures~\ref{eralphapot} and \ref{eralpha}. All three shape similarity methods exhibited poor performance with this query, but many None  of the interaction point queries were significantly better than random. The statistically most significant interaction point query shape-based methods exhibit successful virtual screening which suggests that this particular dataset  is shown in Figure~\ref{cathgip}. The fragment ill-suited  for shape-based screening using  the reference  query is deeply buried within the S1 pocket and the interaction points match the S2 pocket and phosphonate moiety of the ligand. However, there are no interaction points selected in the S3/S4 pocket. Interactions in this pocket can increase affinity, but are not necessary for binding \cite{de_Garavilla_2005}. Unlike whole-ligand similarity methods, molecules. The  shape constraint search can ignore a significant part of queries lacked statistical significance and  the geometry most significant queries identified a large percentage  of the ligand (the naphthalene total compounds  in the S3 pocket) and select for only specific parts of the query shape. In this case, this selectivity results database, resulting  in substantially better virtual screening results that is two orders of magnitude faster than they pre-aligned similarity search methods. longer running times.