deletions | additions       diff --git a/subsection_ER_beta_ER_b__.tex b/subsection_ER_beta_ER_b__.tex  index 9f9debb..bfa3fac 100644  --- a/subsection_ER_beta_ER_b__.tex  +++ b/subsection_ER_beta_ER_b__.tex 
...
\subsection*{ER$\beta$}  ER-b with its best SMARTS expression and approach performed better than random performance Virtual screening results  for both FOMS and VAMS (cite stats table). VAMS performed slightly better than FOMS for ER-b, and the difference was significant (cite stats table). This indicates that VAMS is the best pre-screening estrogen receptor $\beta$ are shown in Figure~\ref{erbeta}. The only  methodfor ER-b.  As might be expected, the binding pocket of ER-b is very similar to ER-a, as is the mode of its inhibition. Similar  to ER-a inhibitors, a hydrogen bond donor/acceptor interaction is necessary for interacting with Glu305 and Arg346. In addition, there outperform random selection  is another hydrogen-bond donor interaction with His475 (cite ER-b). This indicates that both of these interactions RDKit. Although shape constraints  are key to the binding mode capable  of ER-b inhibitors. Only the Glu305 and Arg346 interactions are covered by our SMARTS expressions and fragment pre-alignments. This means that the other key interaction involving the conserved His improving  on thedistal side of the steroid would not be covered. This means that the FOMS approach could accept a compound with only the Glu and Arg interactions, while this type of compound would not actually bind (i. e. pick up a decoy). There are other important hydrophobic interactions evident from the binding of the inhibitor to ER-b, however the interactions mentioned above seem the most important (cite ER-b).   Docking of 1QKN was performed with its active compounds from MUV in smina. This revealed that there were 5 main pockets, with one being covered by the SMARTS expression and fragment pre-alignment. Because there are many pockets that could potentially be filled and weren’t also aligned to, it makes sense that the FOMS approach did not perform as well as VAMS.  The last issue is that rat ER-b was used for analysis. Although there is a high sequence homology, two residues in the ligand-binding cavity differ, which affect the hydrophobic interactions of active compounds. This could have also led to poor  performance for of  both FOMS and VAMS.  More specific SMARTS expressions led to higher performance for both methods (cite table). This makes sense since the SMARTS expression VAMs, they still lack significance  and fragment pre-alignment covered the hydrogen-bonding interactions between Glu305 and Arg346, the most important interactions for ER-b inhibition. generally do not outperform RDKit.