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使用Q2MM预测立体化学

Prediction of Stereochemistry using Q2MM.

作者信息

Hansen Eric, Rosales Anthony R, Tutkowski Brandon, Norrby Per-Ola, Wiest Olaf

机构信息

Department of Chemistry and Biochemistry, University of Notre Dame , Notre Dame, Indiana 46556, United States.

Pharmaceutical Technology and Development, AstraZeneca , Pepparedsleden 1, SE-431 83 Mölndal, Sweden.

出版信息

Acc Chem Res. 2016 May 17;49(5):996-1005. doi: 10.1021/acs.accounts.6b00037. Epub 2016 Apr 11.

DOI:10.1021/acs.accounts.6b00037
PMID:27064579
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4879660/
Abstract

The standard method of screening ligands for selectivity in asymmetric, transition metal-catalyzed reactions requires experimental testing of hundreds of ligands from ligand libraries. This "trial and error" process is costly in terms of time as well as resources and, in general, is scientifically and intellectually unsatisfying as it reveals little about the underlying mechanism behind the selectivity. The accurate computational prediction of stereoselectivity in enantioselective catalysis requires adequate conformational sampling of the selectivity-determining transition state but has to be fast enough to compete with experimental screening techniques to be useful for the synthetic chemist. Although electronic structure calculations are accurate and general, they are too slow to allow for sampling or fast screening of ligand libraries. The combined requirements can be fulfilled by using appropriately fitted transition state force fields (TSFFs) that represent the transition state as a minimum and allow fast conformational sampling using Monte Carlo. Quantum-guided molecular mechanics (Q2MM) is an automated force field parametrization method that generates accurate, reaction-specific TSFFs by fitting the functional form of an arbitrary force field using only electronic structure calculations by minimization of an objective function. A key feature that distinguishes the Q2MM method from many other automated parametrization procedures is the use of the Hessian matrix in addition to geometric parameters and relative energies. This alleviates the known problems of overfitting of TSFFs. After validation of the TSFF by comparison to electronic structure results for a test set and available experimental data, the stereoselectivity of a reaction can be calculated by summation over the Boltzman-averaged relative energies of the conformations leading to the different stereoisomers. The Q2MM method has been applied successfully to perform virtual ligand screens on a range of transition metal-catalyzed reactions that are important from both an industrial and an academic perspective. In this Account, we provide an overview of the continued improvement of the prediction of stereochemistry using Q2MM-derived TSFFs using four examples from different stages of development: (i) Pd-catalyzed allylation, (ii) OsO4-catalyzed asymmetric dihydroxylation (AD) of alkenes, (iii) Rh-catalyzed hydrogenation of enamides, and (iv) Ru-catalyzed hydrogenation of ketones. In the current form, correlation coefficients of 0.8-0.9 between calculated and experimental ee values are typical for a wide range of substrate-ligand combinations, and suitable ligands can be predicted for a given substrate with ∼80% accuracy. Although the generation of a TSFF requires an initial effort and will therefore be most useful for widely used reactions that require frequent screening campaigns, the method allows for a rapid virtual screen of large ligand libraries to focus experimental efforts on the most promising substrate-ligand combinations.

摘要

在不对称过渡金属催化反应中筛选具有选择性的配体的标准方法,需要对配体库中的数百种配体进行实验测试。这种“试错”过程在时间和资源方面成本高昂,而且总体上在科学和智力层面都不尽人意,因为它几乎没有揭示选择性背后的潜在机制。对映选择性催化中立体选择性的准确计算预测需要对决定选择性的过渡态进行充分的构象采样,但必须足够快,以便与实验筛选技术竞争,从而对合成化学家有用。虽然电子结构计算准确且通用,但它们太慢,无法对配体库进行采样或快速筛选。通过使用适当拟合的过渡态力场(TSFFs)可以满足这些综合要求,TSFFs将过渡态表示为一个最小值,并允许使用蒙特卡罗方法进行快速构象采样。量子引导分子力学(Q2MM)是一种自动力场参数化方法,它通过仅使用电子结构计算来最小化目标函数,从而拟合任意力场的函数形式,生成准确的、反应特异性的TSFFs。将Q2MM方法与许多其他自动参数化程序区分开来的一个关键特征是除了几何参数和相对能量之外还使用了海森矩阵。这缓解了TSFFs过拟合的已知问题。通过与测试集的电子结构结果和可用实验数据进行比较来验证TSFF后,可以通过对导致不同立体异构体的构象的玻尔兹曼平均相对能量求和来计算反应的立体选择性。Q2MM方法已成功应用于对一系列从工业和学术角度来看都很重要的过渡金属催化反应进行虚拟配体筛选。在本综述中,我们使用来自不同发展阶段的四个例子,概述了使用Q2MM衍生的TSFFs对立体化学预测的持续改进:(i)钯催化的烯丙基化反应,(ii)锇催化的烯烃不对称二羟基化(AD)反应,(iii)铑催化的烯酰胺氢化反应,以及(iv)钌催化的酮氢化反应。以目前的形式,对于广泛的底物 - 配体组合,计算得到的对映体过量(ee)值与实验值之间的相关系数通常在0.8 - 0.9之间,并且对于给定的底物,可以以约80%的准确率预测合适的配体。虽然生成TSFF需要初始的努力,因此对于需要频繁筛选的广泛使用反应最为有用,但该方法允许对大型配体库进行快速虚拟筛选,以便将实验工作集中在最有前景 的底物 - 配体组合上。

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