Protein-based pharmacophore models derived from protein binding site atoms 
without the inclusion of any ligand information have become more popular in 
virtual screening studies. However, the accuracy of protein-based pharmacophore 
models for reproducing the critical protein-ligand interactions has never been 
explicitly assessed. In this study, we used known protein-ligand contacts from a 
large set of experimentally determined protein-ligand complexes to assess the 
quality of the protein-based pharmacophores in reproducing these critical 
contacts. We demonstrate how these contacts can be used to optimize the 
pharmacophore generation procedure to produce pharmacophore models that 
optimally cover the known protein-ligand interactions. Finally, we explored the 
potential of the optimized protein-based pharmacophore models for pose 
prediction and pose rankings. Our results demonstrate that there are significant 
variations in the success of protein-based pharmacophore models to reproduce 
native contacts and consequently native ligand poses dependent on the details of 
the pharmacophore generation process. We show that the generation of optimized 
protein-based pharmacophore models is a promising approach for ligand pose 
prediction and pose rankings.