Abstract: Herein we will focus on the use of quantum mechanics (QM) in drug design (DD) to solve disparate problems from scoring protein–ligand poses to building QM QSAR models. Through the variational principle of QM we know that we can obtain a more accurate representation of molecular systems than classical models, and while this is not a matter of debate, it still has not been shown that the expense of QM approaches is offset by improved accuracy in DD applications. Objectively validating the improved applicability and performance of QM over classical-based models in DD will be the focus of research in the coming years along with research on the conformational sampling problem as it relates to protein–ligand complexes.

Authors: Kaushik Raha, Martin B. Peters, Bing Wang, Ning Yu, Andrew M. Wollacott, Lance M. Westerhoff, and Kenneth M. Merz Jr.

Reference: Drug Discovery Today. 2007, 12:17-18, 725-731. (see link for full paper).

Abstract: A semiempirical quantum mechanical approach is described for the creation of molecular field-based QSAR models from a set of aligned ligand structures. Each ligand is characterized by a set of probe interaction energy (PIE) values computed at various grid points located near the surface of the ligand. Single-point PM3 calculations afford these PIE values, which represents a pool of independent variables from which multilinear regression models of activity are built. The best n-variable fit is determined by constructing an initial regression using standard forward stepwise selection, followed by refinement using a simulated annealing technique. The resulting fit provides an easily interpreted 3D physical model of ligand binding affinity. Validation against three literature datasets demonstrates the ability of the semiempirical potential to model critical binding interactions in diverse systems.

Authors: Steve Dixon, Kenneth M. Merz, Jr., Giorgio Lauri, and James C. Ianni

Reference: J. Comp. Chem. 2004, 26(1), 23-34. (see link for full paper).