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\section*{Discussion}  The MUV dataset, with its focus on eliminating analogue bias, is particularly resistant to single-query shape-based virtual screens \cite{Tiikkainen_2009}. This is reflected in our overall results, shown in Figure~\ref{aucs}, where only two targets (Rho and PKA) achieve AUCs where the 95\% confidence interval does not overlap with 0.5 (random performance). The remaining targets likely lack meaningful whole-molecule shape complementary between the query ligand and the active compounds of the benchmark. One exception may be HIV-rt, where this is clear early enrichment, indicates that a subset of the actives may be compatible with the query molecule.  optimizing method of rdkit was able to generate better than random AUCs compared to pre-alignment methods (erbeta, factor xia, fak, and hivrt) FOMSalignment methods  dramatically outperformed other methods in two cases (pka, rhok) for the Rho and PKA targets  due to correct position positioning  of a  fragment with key key, conserved  interactions. FOMS provides a rapid means of template docking \cite{Ruiz_Carmona_2014,abagyan2015icm} with shape-based scoring. Choice of fragment critical Shape constraints generally tracked or improved upon the performance of FOMS similarity ranking. For cathg, shape constraints provided the most meaningful virtual screening result as they could select for a subshape of the query ligand while the other methods must rank the full shape.