COMP 208 |
| Adequate treatment of complex interactions, such as pi-cation, non-conventional hydrogen bonding, pi-stacking in ligand -protein/receptor recognition can be crucial to accurate pose prediction in in silico virtual screening of compounds for diverse endpoints such as drug-discovery, predictive metabolism, and toxicity prediction. The ability of this docking/scoring approach to provide good score-based low RMSD discrimination of (correct) biochemically/pharmacologically relevant poses and IC50 correlations is shown with illustrations from several systems: nicotinic acetylcholine receptors and their surrogate binding proteins, kinases, and cytochrome P450s. The use of customized family training to improved pose prediction accuracy and lnKd/ln(IC50) correlations for specific user problems will be discussed as an method to improve discrimination of ligand-target recognition. A critical analysis is presented examining the merits of docking scoring functions as compared to quantum mechanical (QM) and molecular mechanics Poisson-Boltzmann free energy scoring of pharmacologically relevant poses. |
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Drug Discovery
8:30 AM-11:40 AM, Thursday, March 26, 2009 Salt Palace Convention Center -- 258, Oral
Division of Computers in Chemistry |