Prediction of Stereochemistry using Q2MM.
Prediction of Stereochemistry using Q2MM.
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DOI：
10.1021/acs.accounts.6b00037
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发表时间：
2016-05-17
影响因子：
18.3
通讯作者：
Wiest O
中科院分区：
文献类型：
--
作者：
Hansen E;Rosales AR;Tutkowski B;Norrby PO;Wiest O
在不对称、过渡金属催化的反应中筛选配体的选择性的标准方法需要对来自配体库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都很昂贵，而且一般来说，在科学和智力上都不能令人满意，因为它对选择性背后的基本机制揭示得很少。对映选择性催化中立体选择性的精确计算预测需要对决定选择性的过渡态进行足够的构象采样，但必须足够快才能与合成化学家有用的实验筛选技术竞争。虽然电子结构计算是准确的和一般的，他们太慢，允许采样或快速筛选的配体库。通过使用适当拟合的过渡态力场（TSFF）可以满足组合的要求，该力场将过渡态表示为最小值，并允许使用Monte Carlo进行快速构象采样。量子导引分子力学（Q2 MM）是一种自动化力场参数化方法，通过最小化目标函数，仅使用电子结构计算拟合任意力场的函数形式，生成精确的反应特定TSFF。Q2 MM方法区别于许多其他自动参数化过程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Hessian矩阵。这说明了TSFF的过拟合的已知问题。通过比较测试集的电子结构结果和可用的实验数据来验证TSFF之后，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。Q2 MM方法已成功应用于对一系列过渡金属催化的反应进行虚拟配体筛选，这些反应从工业和学术角度都很重要。在这个帐户中，我们提供了一个使用Q2 MM衍生TSFF的立体化学预测的持续改进的概述，使用来自不同发展阶段的四个例子：（i）Pd催化的烯丙基化，（ii）OsO 4催化的烯烃不对称二羟基化（AD），（iii）Rh催化的烯酰胺氢化，和（iv）Ru催化的酮氢化。在目前的形式，计算和实验ee值之间的相关系数为0.8-0.9是典型的广泛的基板-配体组合，和合适的配体可以预测为一个给定的基板与80%的准确度。虽然TSFF的产生需要初始的努力，因此将是最有用的广泛使用的反应，需要频繁的筛选活动，该方法允许快速虚拟筛选大型配体库集中实验努力最有前途的基板-配体组合。
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.