Bryce Richard A, Hillier Ian H
School of Chemistry, University of Manchester, Manchester M13 9PL, UK.
Curr Pharm Des. 2014;20(20):3293-302. doi: 10.2174/13816128113199990601.
The use of computational quantum chemical methods to aid drug discovery is surveyed. An overview of the various computational models spanning ab initio, density function theory, semiempirical molecular orbital (MO), and hybrid quantum mechanical (QM)/molecular mechanical (MM) methods is given and their strengths and weaknesses are highlighted, focussing on the challenge of obtaining the accuracy essential for them to make a meaningful contribution to drug discovery. Particular attention is given to hybrid QM/MM and semiempirical MO methods which have the potential to yield the necessary accurate predictions of macromolecular structure and reactivity. These methods are shown to have advanced the study of many aspects of substrate-ligand interactions relevant to drug discovery. Thus, the successful parametrization of semiempirical MO methods and QM/MM methods can be used to model noncovalent substrate-protein interactions, and to lead to improved scoring functions. QM/MM methods can be used in crystal structure refinement and are particularly valuable for modelling covalent protein-ligand interactions and can thus aid the design of transition state analogues. An extensive collection of examples from the areas of metalloenzyme structure, enzyme inhibition, and ligand binding affinities and scoring functions are used to illustrate the power of these techniques.
本文综述了利用计算量子化学方法辅助药物发现的相关内容。文中概述了各种计算模型,包括从头算、密度泛函理论、半经验分子轨道(MO)以及混合量子力学(QM)/分子力学(MM)方法,并着重强调了它们的优缺点,重点关注获得对药物发现有意义贡献所必需的准确性这一挑战。特别关注了混合QM/MM和半经验MO方法,它们有潜力对大分子结构和反应性做出必要的准确预测。这些方法已推动了与药物发现相关的底物 - 配体相互作用诸多方面的研究。因此,半经验MO方法和QM/MM方法的成功参数化可用于模拟非共价底物 - 蛋白质相互作用,并产生改进的评分函数。QM/MM方法可用于晶体结构优化,对于模拟共价蛋白质 - 配体相互作用尤为有价值,从而有助于过渡态类似物的设计。本文使用了来自金属酶结构、酶抑制、配体结合亲和力和评分函数等领域的大量实例来说明这些技术的强大之处。