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In general, less specific SMARTS expressions performed better for Cathepsin G for both FOMS and VAMS. (explanation for that).  ER-a  ER-a with its best approach and SMARTS expression performed significantly better than random performance for FOMS (cite stat table). For the VAMS method, the best approach and SMARTS expression for ER-a was not significantly better than random performance (cite stat table). This indicates that for ER-a, FOMS was a better performing screening method.   The most important interaction for an ER-a inhibitor is the 3-hydroxy interaction with Glu 353 and stacking effect of the phenyl group with Phe 404 (cite crystal, ER-a). Although these interactions were covered by our SMARTS expression and fragment pre-alignment, another important interaction occurring between a 17-hydroxy group and His 524 was not covered by either our SMARTS selection or fragment pre-alignment (cite crystal ER-a). Higher selectivity can also come from the addition of a group that performs hydrogen bonding with Leu 384. Because only two of the important interactions that ER-a inhibitors need to perform are covered by the SMARTS and pre-alignment, it makes sense that FOMS did not perform better than random chance (refer to Table I will make).   When docking with smina was performed on 2IOG and its set of active compounds from MUV, the two main interactions observed were with Glu353 and Arg394 by one hydrogen-bond donor/acceptor (visual inspection). This indicates that both residues are important to interact with for an ER-a inhibitor. Our fragment selection and pre-alignment covered this interaction. There were also interactions with Asp351 observed on the distal side with a hydrogen-bond donor, sometimes concurrent with the previous interactions. This indicates that this interaction is also key for the binding mode of ER-a inhibitors, and was not covered by our fragment pre-alignment. There were also 5 pockets that could potentially be filled. Our pre-alignment only covered the most occupied pocket among all active conformers docked.   More specific SMARTS expressions performed better for FOMS, but not VAMS for ER-a inhibitors (cite Table). For FOMS, the best fragment selection included pre-alignment to a phenolic SMARTS expression, which would align to the region that could form the interactions with Glu353 and Arg394. For this reason, it makes sense that this SMARTS expression and pre-alignment would produce the best performance for an ER-a inhibitor. (Explanation of why VAMS had better performance with lower specificity)    ER-a-p  ER-a with its best approach and SMARTS expression did not perform significantly better than random performance for the FOMS or VAMS method (cite table). Although FOMS was significantly better performing 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 interaction with Glu353 and Arg394 is vital for agonism of ER-a (cite crystal ER-a). There is also an indication that the stereochemistry is an important consideration for ER-a agonism when trying to avoid steric clash with W383, L536, and L539. The study reporting the crystal structure of 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 and fragment pre-alignments did not perform significantly better than random chance (cite table).   Docking of 1XP9 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, there were 5 pockets available for filling. Three of these pockets were occupied by almost every conformer of the active compounds, with one of the main pockets being covered by fragment pre-alignment. The variation in which pockets needed to be filled indicates that there are a plethora of hydrophobic interactions that need to occur for ER-a agonism. This could explain the poor performance of both methods since it only pre-aligned to one of the many pockets.   For ER-a agonists, more specific SMARTS expressions had better performance for FOMS and VAMS (cite table). The best performing SMARTS expression covered the interaction with Glu353 and Arg394. This is what most likely gave it the best performance since interaction with these two residues are vital for ER-a agonism. (explanation for VAMS performance)  ER-b  ER-b with its best SMARTS expression and approach performed better than random performance 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 method for 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 is another hydrogen-bond donor interaction with His475 (cite ER-b). This indicates that both of these interactions are key to the binding mode 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 on the distal 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. 
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More specific SMARTS expressions led to higher performance for both methods (cite table). This makes sense since the SMARTS expression and fragment pre-alignment covered the hydrogen-bonding interactions between Glu305 and Arg346, the most important interactions for ER-b inhibition.    FAK  FAK with its best SMARTS expression and approach performed significantly better than random performance for the FOMS method (cite stat table). Even with the best SMARTS expression and approach, the VAMS method performed worse than random performance for FAK (cite table).   FAK inhibition relies on hydrogen-bonding interactions with Cys502 and Arg426, along with an important hydrophobic interaction with Leu553 in the active site (cite FAK). Only the interaction with Cys502 is covered by the SMARTS expression and fragment pre-alignment. This indicates that other decoy compounds could be scored more favorably with FOMS since it would only focus on the fragment choice that highlights one of 3 key interactions. FAK inhibitors are supposed to be ATP-competitive, so it makes sense that only a small piece of the inhibitor cannot represent the entire binding mode of inhibition.   When docking with smina was performed on 3BZ3 and the active compounds that were covered by our SMARTS expressions, there were 5 pockets possible for filling observed, with only one covered by our fragment pre-alignment. In addition, hydrogen-bonding with Cys502 and Arg426 were observed frequently between active conformers. Our fragment pre-alignment and SMARTS expression would only cover the interaction with Cys502. Because there are more than one interaction and many pockets to be filled, it makes sense that FOMS was not able to yield high performance.  
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More specific SMARTS expressions led to higher performance for VAMS, whereas the least specific SMARTS expression led to highest performance for FOMS (cite table). (explanation for both?)  HIV-RT  The best SMARTS expression and approach for HIV-RT did not perform significantly better than random performance for both methods (cite table). Inhibition of HIV-RT relies on many key interactions, including a stacking interaction with Tyr188, hydrogen-bonding with Lys103, and many important hydrophobic interactions with Val and Phe residues (cite HIV). Single and double mutation varieites of HIV-RT were tested against known inhibitors, with the conserved Tyr residue mutation causing a 600 fold increase in IC50. Our original fragment pre-alignment and SMARTS expression only covered the conserved Lys interaction, however we then performed pre-alignment to the Tyr residue interaction and observed better performance (cite Table). As with most of the other targets in the MUV dataset, there are multiple interactions necessary for proper binding of an HIV-RT inhibitor. FOMS will only cover a small part of the necessary interaction.   Docking with 3DRP in smina for the active compounds from the MUV dataset were performed. This revealed 4 potential pockets to fill, with only one being covered by our fragment pre-alignment. In addition, hydrogen-bonding with Lys103 and potential stacking with Tyr188 were observed frequently. This indicates the importance of both interactions for proper HIV-RT binding. Since only one of the 4 pockets are covered by our fragment pre-alignment, it is possible that this is a contributing factor to the poor performance of FOMS.  Less specific SMARTS expressions led to higher performance for FOMS, whereas more specific SMARTS expressions led to higher performance for VAMS (cite table). (explanation for both?)    HSP 90  The best SMARTS expression and approach for Hsp90 performed significantly better than random performance for FOMS, but not for VAMS (cite table). Hsp 90 inhibitors interact with the ATP binding site. The most important interactions are with Asp93, Gly97, Lys58 , and Asn51 residues, with the study reporting the crystal structure highlighting Asp93 as the most important (cite HSP). Only the Asp93 interaction is covered by our SMARTS expressions and fragment pre-alignments, while the other interactions are also key for Hsp90 inhibition, with the Gly backbone being second in importance. As with other targets in the MUV dataset, the poor performance of FOMS relative to random performance can be partially attributed to the large number of interactions that are not covered by our fragment selection due to the nature of the ligand binding mode.  Docking with 2VCI in smina for the active compounds from the MUV dataset were performed. This revealed 4 potential hydrophobic pockets, with only one covered by our fragment pre-alignment. There were also many active conformers hydrogen-bonding with Asp93 and at least one other residue as well. These docking results indicate the importance of inclusion of every hydrogen-bonding interaction and more than one hydrophobic pocket for Hsp90 inhibition. Our SMARTS expression and fragment pre-alignment only covered one of these interactions and one of the pockets, which most likely led to FOMS not having high performance.   More specific SMARTS expressions led to higher performance for FOMS and VAMS (cite table). This makes sense as the more specific fragment choice would help both methods select compounds that can make the hydrogen-bond with Asp93.  PKA  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).  Rho-Kinase2  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).