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: Computational methods to calculate binding affinity in protein-ligand interaction are of immense interest because of obvious practical applications in structure-based drug design. Scoring functions attempt to calculate the variation in binding affinity of ligands-inhibitors bound to protein targets at various levels of theory. In this study we use semiempirical quantum mechanics to design a scoring function that can calculate the electrostatic interactions and solvation free energy expected during complexation. This physically based approach has the ability to capture binding affinity trends in a diverse range of protein-ligand complexes. We also show the predictive power of this scoring function within protein targets and its ability to score ligand poses docked to a protein target. We also demonstrate the ability of this scoring function to discriminate between native and decoy poses and highlight the crucial role played by electrostatic interactions in molecular recognition. Finally we compare the performance of our scoring function with other available scoring functions in the literature.

Authors: Kaushik Raha and Kenneth M. Merz, Jr.

Reference: J. Med. Chem. 2005, 48, 4558-4575. (see link for full paper).

Abstract: In this communication, we report the development of a novel quantum mechanics-based scoring function to predict free energy of ligand binding in the zinc metalloenzymes carbonic anhydrase (CA) and carboxypeptidase A (CPA). In particular, the AM1 method is used in conjunction with solvation modeling to predict the relative binding affinities of 18 CA and 5 CPA inhibitors. The effect of metal-ligand charge transfer is also discussed and shown to be different in CPA and CA, providing a further challenge to computing metalloenzyme binding affinities.

Authors: Kaushik Raha and Kenneth M. Merz, Jr.

Reference: J. Am. Chem. Soc. 2004, 126(4), 1020-1021. (see link for full paper).