Holistic prediction of enantioselectivity in asymmetric catalysis
Holistic prediction of enantioselectivity in asymmetric catalysis
复制标题
DOI：
10.1038/s41586-019-1384-z
复制
发表时间：
2019-07-18
期刊：
影响因子：
64.8
通讯作者：
Sigman, Matthew S.
中科院分区：
文献类型：
--
作者：
Reid, Jolene P.;Sigman, Matthew S.
当面临不熟悉的反应空间时，合成化学家通常会使用一种新的底物类型，将已报道的成功反应的条件(试剂、催化剂、溶剂和添加剂)应用于所需的密切相关的反应。不幸的是，由于反应要求的细微差别，这种方法经常失败。因此，合成化学中的一个重要目标是能够将化学观察定量地从一个反应转移到另一个反应。在这里，我们提出了一个整体的、数据驱动的工作流程，用于推导一组反应的统计模型，这些模型可以用于预测样本外的反应。作为一个验证的案例研究，我们结合了已发表的使用1，1‘-双-2-萘酚(BINOL)衍生的手性磷酸进行一系列与亚胺的亲核加成反应的对映体选择性数据集，并开发了统计模型。这些模型揭示了给予不对称诱导的一般相互作用，并允许将这种信息定量地转移到新的反应成分。这项技术为将综合反应分析转换到不同的化学空间创造了机会，简化了催化剂和反应开发。
When faced with unfamiliar reaction space, synthetic chemists typically apply the reported conditions (reagents, catalyst, solvent and additives) of a successful reaction to a desired, closely related reaction using a new substrate type. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Consequently, an important goal in synthetic chemistry is the ability to transfer chemical observations quantitatively from one reaction to another. Here we present a holistic, data-driven workflow for deriving statistical models of one set of reactions that can be used to predict out-of-sample reactions. As a validating case study, we combined published enantioselectivity datasets that employ 1,1'-bi-2-naphthol (BINOL)-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines and developed statistical models. These models reveal the general interactions that impart asymmetric induction and allow the quantitative transfer of this information to new reaction components. This technique creates opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.