deletions | additions       diff --git a/section_Results_General_Observations_Protein__.tex b/section_Results_General_Observations_Protein__.tex  index 0c00d6c..43c49bd 100644  --- a/section_Results_General_Observations_Protein__.tex  +++ b/section_Results_General_Observations_Protein__.tex 
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In general, FOMS performed better than VAMS for every protein target. This indicates that it is an effective pre-screen, when considering the earlier success of VAMS (cite VAMS). In general, more specific SMARTS expressions led to a higher performance for both FOMS and VAMS. Higher specificity of fragment choice will lead to identifying singly the most important interactions involved in the binding mode of the ligand. In general, the interaction point method for both FOMS and VAMS yielded the highest performance (cite table). The interaction point method is able to include specific areas where interactions are likely to occur between the receptor and ligand. Logically, this approach should yield the highest performance since it uses the most information involved in ligand-receptor interactions.     FOMS does nearly as well as or better than VAMS.  FOMS worse than rdkit in 7  cathg - all random, but shape constraints do significantly better  eralpha - all random  eralpha-pot - all random (or worse)  erbeta - rdkit wins, FOMS=VAMS (0.43), lack of significance for sc  hivrt - almost random or worse, rdkit (0.57), shape constraints match rdkit, FOMS > VAMS, only one with good early enrichment  fxia - rdkit wins, all other lousy  hsp90 - foms and vams basically random, vams worse, sc not impressive   rho - fantastic, FOM >> VAMS > rdkit, shape constraints even better  pka - fantastic, FOM >> VAMS > rdkit, shape constraints even better  but totally different resutls for pka.f5