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基于结构的虚拟筛选和先导化合物优化中的量子化学方法。

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

作者信息

Cavasotto Claudio N, Adler Natalia S, Aucar Maria G

机构信息

Laboratory of Computational Chemistry and Drug Design, Instituto de Investigación en Biomedicina de Buenos Aires, CONICET, Partner Institute of the Max Planck Society, Buenos Aires, Argentina.

出版信息

Front Chem. 2018 May 29;6:188. doi: 10.3389/fchem.2018.00188. eCollection 2018.

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.

摘要

如今,计算化学已成为药物先导发现工作中的一种成熟工具。由于方法学的发展以及计算机硬件的巨大进步,基于量子力学(QM)的方法在过去十年中受到了广泛关注,对生物大分子的计算也越来越多地被探索,旨在更准确地描述蛋白质 - 配体相互作用并预测结合亲和力。原则上,量子力学公式包含了对能量的所有贡献,涵盖了分子力学力场中通常缺失的项,如电子极化效应、金属配位和共价键合;此外,量子力学方法可以系统地改进,并且具有更高的可转移性。在本综述中,我们展示了基于显式量子力学方法在小分子对接和评分以及蛋白质 - 配体系统结合自由能计算中的最新应用。尽管在工业药物先导发现环境中常规使用基于量子力学的方法仍然是一项艰巨的挑战性任务,但它们很可能会在药物发现流程中越来越多地成为积极参与者。

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Predicting Binding Free Energies: Frontiers and Benchmarks.预测结合自由能:前沿和基准。
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J Chem Theory Comput. 2017 May 9;13(5):2245-2253. doi: 10.1021/acs.jctc.6b01217. Epub 2017 Apr 5.
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