Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization

Abstract

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

“…The use of quantum mechanical methods can improve the description of protein–ligand interactions and, in principle, could provide a more accurate binding affinity ( Raha and Merz, 2005 ; Chaskar et al, 2017 ; Crespo et al, 2017 ; Cavasotto et al, 2018 ). This is particularly true when dealing with systems where the molecular recognition involves bond formation, π-stacking, cation-π, halogen bonding (i.e., σ-hole bonding), and polarization and charge transfer effects ( Christensen et al, 2016 ).…”

“…These advances were essential to overcome the bottleneck of the high computational cost and are allowing the increasing use of QM methods in the prediction of protein–ligand binding affinities ( Crespo et al, 2017 ). Recent high-quality reviews cover applications of explicit QM calculations in lead identification and optimization ( Adeniyi and Soliman, 2017 ; Crespo et al, 2017 ; Cavasotto et al, 2018 ), development of QM methods for ligand binding affinity calculations ( Ryde and Söderhjelm, 2016 ), and development of semi-empirical QM methods for non-covalent interactions ( Christensen et al, 2016 ; Yilmazer and Korth, 2016 ).…”

Recent Progresses in Ab Initio Electronic Structure Calculation toward Understandings of Functional Mechanisms of Biological Macromolecular Systems

“…Great progresses have been achieved up to date for improvement of QM/MM calculation algorithms and their applications to biological systems [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Importance of the environments has been reported from many QM/MM studies.…”

Computational chemistry in drug lead discovery and design

“…Until recently, the field of macromolecular simulation—and in particular, binding free energy calculation—has been mainly dominated by methods based on classical mechanics, but in recent years there has been a surge in the development of QM‐based methods aimed at structure‐based drug design. It should be stressed that the QM formulation is, in principle, theoretically exact, since it includes all contributions to the energy, including those effects usually missing in FFs, such as electronic polarization, charge transfer, halogen bonding, and covalent‐bond formation; moreover, it avoids system‐dependent parameterizations, so that all elements and interactions are considered on equal footing (recent theoretical developments and applications of QM‐based methods to ligand binding and virtual screening can be found elsewhere).…”