The pharmacophore filtering method begins by using a docking program in the ordinary way, for pose generation and alignment in the binding site.
Scoring function is at the heart of molecular docking by assisting a docking program to efficiently explore the binding space of a ligand.
This method uses a docking program for pose generation without regard to scoring, followed by filtering with receptor-based pharmacophore searches.
Inspired by the promising results in ligand pose prediction and pose ranking using protein-based pharmacophore models, we extended the pharmacophore-based matching and scoring scheme into a docking program, named PharmDock (Pharmacophore-based Docking).
Generally, a docking program consists of two main components the sampling algorithm and the scoring function.
In contrast, the pharmacophore filtering method presented here requires no additional new or proprietary software unlike these other methods for post-processing docking results – only a docking program and a pharmacophore generation program, both of which are highly likely to be in use by any modeling group.
GOLD (Genetic Optimization for Ligand Docking), a docking program based on genetic algorithm [39] [42] was used to dock the ligands to the protein active sites.
In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand.
We have shown here that, compared to simply ranking the poses output by the docking program according to a single docking score, an initial filtering of the docked poses with a pharmacophore query to remove poses that do not form certain essential interactions with the target binding site greatly improves the quality of the results.
In their method, the conventional structure-based screening was performed by a protein-compound docking program such as Glide, FlexX (Rarey et al 1996), or GOLD (Jones et al 1997).
