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David Koes edited Discussion.tex
over 8 years ago
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\section*{Discussion}
mixed results, many benchmarks resistant to any shape based query (eralpha, hsp90)
optimizing method of rdkit was able to generate better than random AUCs compared to pre-alignment methods (erbeta, factor xia, fak, and hivrt)
FOMS alignment methods dramatically outperformed other methods in two cases (pka, rhok) due to correct position of fragment with key interactions. FOMS provides a rapid means of template docking 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.
The primary advantages of shape-based fragment alignment search are threefold. Biasing the alignment to the desired fragment position. This is consistent with other results that demonstrate the importance of adding pharmacophoric properties (or `color') to shape similarity. Fragment alignment introduces a hard bias toward the specific fragment alignment without introducing any additional computation or calculation, as with color methods. In fact, as we have shown, pre-alignment substantially improves performance, which is the second main advantage. Prealignment, whether to fragments (FOMS) or canonical internal coordinates (VAMS) is orders of magnitude faster
