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\subsection*{Rho-Kinase2}  Virtual screening results Rho-Kinase2 are shown in Figure~\ref{rho} and are similar to those of PKA. FOMS achieves an AUC of 0.94, VAMS an AUC of 0.56, and RDKit and AUC of 0.34. The best shape constraints match or exceed the performance of FOMS. As with PKA, if a less selective fragment is used (a generic aromatic ring), virtual performance is substantially reduced (data not shown), although, as with PKA, FOMS still outperforms RDKit and VAMS in this case.  The best SMARTS expression and approach for Rho-Kinase2 performed significantly better than random performance for both FOMS and VAMS (cite stats table). The FOMS approach was significantly better than the VAMS approach for Rho-Kinase2 (cite stats table).   Similar to PKA, Rho-Kinase2 inhibitors are relatively small compared to other inhibtors and have few electrostatic interactions. In the case of 2H9V and its inhibitor, there are three hydrogen-bonding interactions with conserved Met, Asn, and Asp. There are also important hydrophobic interactions with other residues inside the pocket (cite Rho). Our SMARTS expression and fragment pre-alignment covers the hydrogen-bonding interaction with the Met and important hydrophobic interactions with conserved Val and Leu residues. This most likely led to the high performance of FOMS for Rho-Kinase2 (cite table).  % The best SMARTS expression and approach for Rho-Kinase2 performed significantly better than random performance for both FOMS and VAMS (cite stats table). The FOMS approach was significantly better than the VAMS approach for Rho-Kinase2 (cite stats table).   %Similar to PKA, Rho-Kinase2 inhibitors are relatively small compared to other inhibtors and have few electrostatic interactions. In the case of 2H9V and its inhibitor, there are three hydrogen-bonding interactions with conserved Met, Asn, and Asp. There are also important hydrophobic interactions with other residues inside the pocket (cite Rho). Our SMARTS expression and fragment pre-alignment covers the hydrogen-bonding interaction with the Met and important hydrophobic interactions with conserved Val and Leu residues. This most likely led to the high performance of FOMS for Rho-Kinase2 (cite table).  %  More specific SMARTS expressions led to lower performance for both the FOMS and VAMS methods. (explanation for why this is ). diff --git a/begin_table_centering_label_fragtable__.tex b/begin_table_centering_label_fragtable__.tex  index 25398d5..2b5dea6 100644  --- a/begin_table_centering_label_fragtable__.tex  +++ b/begin_table_centering_label_fragtable__.tex 
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\begin{table*} \centering  \label{fragtable}  \begin{tabular}{ c c cc  c} Fragment & SMARTS & Confirmatory Assay ID & Actives & Decoys \\   \hline  CathG$_3$ CathG  & c1ccccc1[!H] & 832 & 24 & 12847 \\ % $_3$  ER$\alpha$ agonist$_2$ agonist  & c1ccccc1[!H] & 737 & 29 & 13390 \\ ER$\alpha_3$ %$_2$  ER$\alpha$  & c1ccccc1[!H] & 713 & 22 & 13063 \\ ER$\beta_3$ %_3  ER$\beta$  & c1ccccc1[!H] & 733 & 23 & 12839 \\ FAK$_3$ %_3  FAK  & a1aaaaa1[!H] & 810 & 29 & 13891 \\ FXIa$_4$ % $_3$  FXIa  & a1aaaa1[!H] & 846 & 24 & 7691 \\ HIVrt$_4$ %$_4$  HIVrt  & a1aaaa1[!H] & 652 & 18 & 9535 \\ HSP90$_2$ %$_4$  HSP90  & c1ccccc1[!H] & 712 & 29 & 12915 \\ PKA$_1$ % $_2$  PKA  & c1[c,n]cccn1 & 466 & 25 & 5582 \\ PKA$_5$ % $_1$  PKA$_\mathrm{alt}$  & a1aaaaa1[!H] & 466 & 29 & 13985 \\ Rho$_1$ % $_5$  Rho  & c1[c,n]cccn1 & 644 & 18 & 4593 \\ % $_1$  \end{tabular}   \caption{SMARTS expressions for anchor fragments evaluated in each MUV benchmark shown with the number of active and decoy compounds from the benchmark that contain that fragment. Every MUV benchmark starts with 30 active compounds and 15000 decoys. }   \end{table*}  diff --git a/figures/pka.f5.time/caption.tex b/figures/pka.f5.time/caption.tex  index 94a9cb5..44153f9 100644  --- a/figures/pka.f5.time/caption.tex  +++ b/figures/pka.f5.time/caption.tex 
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%endfig 0.48  %bigcap  %fullfig  \label{pka5} pka5 Protein kinase A virtual screening performance and search times for various methods with a less selective fragment choice.  diff --git a/layout.md b/layout.md  index 51d7e15..f5797d0 100644  --- a/layout.md  +++ b/layout.md 
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figures/pka.f.time/pka.f.time.png  figures/pka.f5/pka.f5.png  figures/pka.f5.time/pka.f5.time.png  subsection_Protein_Kinase_A_The__.tex subsection_Protein_Kinase_A_Virtual__.tex  figures/rho.f/rho.f.png  figures/rho.f.time/rho.f.time.png  Results.tex  diff --git a/subsection_Protein_Kinase_A_The__.tex b/subsection_Protein_Kinase_A_The__.tex  deleted file mode 100644  index b2c337d..0000000  --- a/subsection_Protein_Kinase_A_The__.tex  +++ /dev/null 
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\subsection*{Protein Kinase A}  The best SMARTS expression and approach for PKA performed significantly better than random performance for both FOMS and VAMS (cite stats table). The FOMS approach was significantly better than the VAMS approach for PKA (cite stats table).  Like many other targets in this dataset, PKA inhibitors interact with the ATP binding site. These inhibitors are relatively smaller in size compared to other inhibitors and have few electrostatic interactions. In the example of 1Q8U and its inhibitor, there is only one hydrogen bond with Val123, where the rest of the ligand makes multiple Van der Waals contacts with other parts of the target (cite PKA). SMARTS expressions and fragment pre-alignment covers the only electrostatic interaction and most of the hydrophobic interactions involved in PKA inhibitors, which most likely led to the high performance of FOMS (cite table).  More specific SMARTS expressions led to higher performance for FOMS (cite table). This makes sense as the more specific fragment choice would cover the only necessary hydrogen-bond interaction with the receptor. Less specific SMARTS expressions led to higher performance for the VAMS method (cite table). (explanation for why this is).  diff --git a/subsection_Protein_Kinase_A_Virtual__.tex b/subsection_Protein_Kinase_A_Virtual__.tex  new file mode 100644  index 0000000..009a321  --- /dev/null  +++ b/subsection_Protein_Kinase_A_Virtual__.tex 
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\subsection*{Protein Kinase A}  Virtual screening results for protein kinase A (PKA) are shown in Figure~\ref{pka}. For this kinase FOMS substantially outperforms both RDKit (AUC 0.36) and VAMS (AUC 0.55) with an AUC of 0.90. Shape constraints can perform even better than the full similarity search. Much of this performance is due to the use of a more selective fragment that properly orients the nitrogen in the ring fragment. When a more generic fragment, an arbitrary aromatic ring (PKA$_\mathrm{alt}$ in Table~\ref{fragtable}), is used as shown in Figure~\ref{pka5} the enhanced performs of FOMS disappears and the ability of shape constraints to generated enriched subsets is reduced. This use of this fragment pulls in thousands more decoys and introduces many more valid fragment alignments for both actives and decoys. This implies that the good performance of FOMS and shape constraints with this target is likely due the ability of the fragment to properly position compounds to make conserved interactions with the hinge region of the kinase, as shown in Figure~\ref{targets}. Compounds that contain the specified fragment but cannot position the fragment to make these interactions while maintaining the steric compatibility of the whole ligand with the query ligand and receptor are filtered out.  % The best SMARTS expression and approach for PKA performed significantly better than random performance for both FOMS and VAMS (cite stats table). The FOMS approach was significantly better than the VAMS approach for PKA (cite stats table).  %Like many other targets in this dataset, PKA inhibitors interact with the ATP binding site. These inhibitors are relatively smaller in size compared to other inhibitors and have few electrostatic interactions. In the example of 1Q8U and its inhibitor, there is only one hydrogen bond with Val123, where the rest of the ligand makes multiple Van der Waals contacts with other parts of the target (cite PKA). SMARTS expressions and fragment pre-alignment covers the only electrostatic interaction and most of the hydrophobic interactions involved in PKA inhibitors, which most likely led to the high performance of FOMS (cite table).  %More specific SMARTS expressions led to higher performance for FOMS (cite table). This makes sense as the more specific fragment choice would cover the only necessary hydrogen-bond interaction with the receptor. Less specific SMARTS expressions led to higher performance for the VAMS method (cite table). (explanation for why this is).