Development of Efficient Molecular Mechanics Methods for Application in Drug Discovery and Design.

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Discovering a new drug is an expensive and laborious process, costing billions dollars and well over 10 years. This process starts with the identification of promising drug candidates for a disease related target macromolecule. For optimal efficiency, structure-based (protein or nucleic acid target) drug design (SBDD) methods are now commonly. For example, virtual screening, employing computational docking methods and/or molecular dynamic simulations, allows the prediction of the binding mode of small molecules within the macromolecule and their binding affinity. However, several other properties must be optimized including reduced toxicity and optimal bioavailability. As some of these properties are also associated to the binding to macromolecules (involved in other biological processes), docking and molecular dynamic simulations are, once more, a method of choice. As many of these methods rely on molecular mechanics and optimal definition of the small molecules, improving these should improve the accuracy of the predictions. We propose herein to expand our molecular mechanics method H-TEQ, to develop a pKa predictor and an atomic charge predictor. All of these properties, when implemented into our docking program and/or molecular dynamics simulation methods should significantly improve their accuracy.