Detailed structural and energetic results from computational modeling can be used to help understand and rationalize origins of inhibitor binding to wild type targets and variants which lead to drug resistance. This talk will present examples in which all-atom molecular dynamics simulation methods were used in conjunction with free energy calculations to study binding for inhibitors targeting neuraminidase (influenza), epidermal growth factor receptor (cancer), and gp41 (HIV). Parallel efforts to improve virtual screening methods will also be discussed which include algorithmic additions/changes to the program DOCK, evaluation of pose identification success rates using a large validation set of ligand-receptor complexes, and a new footprint-based scoring function geared to identify molecules which resemble that of a known reference.
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