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

被引:51
|
作者
Cavasotto, Claudio N. [1 ]
Adler, Natalia S. [1 ]
Aucar, Maria G. [1 ]
机构
[1] Consejo Nacl Invest Cient & Tecn, Max Planck Soc, Inst Invest Biomed Buenos Aires, Lab Computat Chem & Drug Design,Partner Inst, Buenos Aires, DF, Argentina
来源
FRONTIERS IN CHEMISTRY | 2018年 / 6卷
关键词
quantum mechanics; semi-empirical methods; structure-based drug design; molecular docking; drug lead optimization; binding free energy; molecular dynamics; SEMIEMPIRICAL QM METHODS; LIGAND-BINDING; SCORING FUNCTION; MOLECULAR DOCKING; PROTEIN; QM/MM; MECHANICS; AFFINITY; PREDICTION; SOLVATION;
D O I
10.3389/fchem.2018.00188
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
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页数:7
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