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三价硼化合物亲核性的定量构效关系

Quantitative Structure-Activity Relationships for the Nucleophilicity of Trivalent Boron Compounds.

作者信息

García-López Diego, Cid Jessica, Marqués Ruben, Fernández Elena, Carbó Jorge J

机构信息

Departament de Química Física i Inorgànica, Universitat Rovira i Virgili, Marcel⋅lí Domingo 1, 43007, Tarragona, Spain.

出版信息

Chemistry. 2017 Apr 11;23(21):5066-5075. doi: 10.1002/chem.201605798. Epub 2017 Mar 28.

DOI:10.1002/chem.201605798
PMID:28177532
Abstract

We describe herein the development of quantitative structure-activity relationships (QSAR) for the nucleophilicity of trivalent boron compounds covering boryl fragments bonded to alkali and alkaline-earth metals, to transition metals, and to sp boron units in diboron reagents. We used the charge of the boryl fragment (q[B]) and the boron p/s population ratio (p/s) to describe the electronic structures of boryl moieties, whereas the distance-weighted volume (V ) descriptor was used to evaluate the steric effects. The three-term easy-to-interpret QSAR model showed statistical significance and predictive ability (r =0.88, q =0.83). The use of chemically meaningful descriptors has allowed identification of the factors governing the boron nucleophilicity and indicates that the most efficient nucleophiles are those with enhanced the polarization of the B-X bond towards the boron atom and reduced steric bulk. A detailed analysis of the potential energy surfaces of different types of boron substituents has provided insight into the mechanism and established an order of nucleophilicity for boron in B-X: X=Li>Cu>B(sp )>Pd. Finally, we used the QSAR model to make a priori predictions of experimentally untested compounds.

摘要

我们在此描述了三价硼化合物亲核性的定量构效关系(QSAR)的发展情况,这些三价硼化合物涵盖了与碱金属和碱土金属、过渡金属以及二硼试剂中的sp硼单元相连的硼基片段。我们使用硼基片段的电荷(q[B])和硼的p/s电子云密度比(p/s)来描述硼基部分的电子结构,而距离加权体积(V)描述符则用于评估空间效应。这个易于解释的三项QSAR模型具有统计学意义和预测能力(r = 0.88,q = 0.83)。使用具有化学意义的描述符能够确定控制硼亲核性的因素,并表明最有效的亲核试剂是那些增强了B-X键向硼原子的极化且空间位阻减小的试剂。对不同类型硼取代基的势能面进行详细分析,有助于深入了解其机理,并确定了硼在B-X中的亲核性顺序:X = Li > Cu > B(sp) > Pd。最后,我们使用QSAR模型对未经实验测试的化合物进行了先验预测。

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