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| Mark McGann received his B.S. from Rensselear Polytechnic Institute in the Spring of 1993. He then spent six months as a quality assurance engineer at Rogers Materials Molding division before entering graduate school at Tulane University Chemical Engineering department in January 1994. His graduate research focused on detailed molecular simulations of polymer crystals. Mark graduated from Tulane with a Ph.D. in Chemical Engineering in the fall of 1998 and took a position at the Johnson & Johnson Pharmaceutical Research Institute where he developed fast docking programs for structure-based drug design in collaboration with OpenEye. He joined OpenEye in October 2000 where he still works as the principal developer of OpenEye's docking software F.R.E.D.. Mark has the most amazing collection of video games anyone has ever seen.
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New Docking Methods for Pose Prediction and Enrichment
Mark McGann, PhD, OpenEye Scientific Software, 222rd Street Suite 3211
Cambridge, MA 02142, USA
The two basic jobs of a docking program are determining the correct position of ligands within the active site and correctly ranking the relative activities of those ligands. In terms of the former, many docking programs can reliably produce a set of potential poses in which at least one pose can be considered correctly docked. Reliably picking this single correct pose out of the entire set with a scoring function is often more problematic. This presentation describes a new method of picking out the correctly docked pose using multiple scoring functions. This "consensus structure" method is distinct from what is generally called "consensus scoring" because the former is used to determine the docked structure while the latter is used for enrichment. While the "consensus structure" method is a structure determination method, it also indirectly improves enrichment by virtue of its improved pose prediction. This presentation will also detail the use of MASC to more directly improve enrichments. The MASC method attempts to correct for systematic errors in any scoring function by comparing a ligand's score to that ligand's score in a set of reference protein targets. A corrected score is then assigned based upon how much better or worse the ligand scores in the current target relative to the reference targets. Conceptually this is equivalent to calculating the average delta G of moving a ligand from the reference targets into the current target,
rather than calculating the delta G of moving the ligand from solvent into the current target.
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