Protein-based pharmacophore models are enriched with the information of potential interactions between ligands and the protein target. We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking. In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function.
Tests of PharmDock on ligand pose prediction, binding affinity estimation, compound ranking and virtual screening yielded comparable or better performance to existing and widely used docking programs. The docking program comes with an easy-to-use GUI within PyMOL. Two features have been incorporated in the program suite that allow for user-defined guidance of the docking process based on previous experimental data. Docking with those features demonstrated superior performance compared to unbiased docking.
A protein pharmacophore-based docking program, PharmDock, has been made available with a PyMOL plugin. PharmDock and the PyMOL plugin are freely available fromhttp://people.pharmacy.purdue.edu/~mlill/software/pharmdock webcite.
Protein pharmacophores; Docking; Scoring; Biased docking; Constraint docking; Confined docking; GUI; PyMOL
Date of this Version
Hu, Bingjie and Lill, Markus A., "PharmDock: A Pharmacophore-Based Docking Program" (2014). Department of Medicinal Chemistry and Molecular Pharmacology Faculty Publications. Paper 11.