Milo Anat, Neel Andrew J, Toste F Dean, Sigman Matthew S
Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, UT 84112, USA.
Chemical Sciences Division, Lawrence Berkeley National Laboratory, and Department of Chemistry, University of California, Berkeley, CA 94720, USA.
Science. 2015 Feb 13;347(6223):737-43. doi: 10.1126/science.1261043.
Knowledge of chemical reaction mechanisms can facilitate catalyst optimization, but extracting that knowledge from a complex system is often challenging. Here, we present a data-intensive method for deriving and then predictively applying a mechanistic model of an enantioselective organic reaction. As a validating case study, we selected an intramolecular dehydrogenative C-N coupling reaction, catalyzed by chiral phosphoric acid derivatives, in which catalyst-substrate association involves weak, noncovalent interactions. Little was previously understood regarding the structural origin of enantioselectivity in this system. Catalyst and substrate substituent effects were probed by means of systematic physical organic trend analysis. Plausible interactions between the substrate and catalyst that govern enantioselectivity were identified and supported experimentally, indicating that such an approach can afford an efficient means of leveraging mechanistic insight so as to optimize catalyst design.
了解化学反应机理有助于优化催化剂，但从复杂系统中提取这些知识往往具有挑战性。在此，我们提出了一种数据密集型方法，用于推导并预测性地应用对映选择性有机反应的机理模型。作为一个验证案例研究，我们选择了一种由手性磷酸衍生物催化的分子内脱氢C-N偶联反应，其中催化剂与底物的缔合涉及弱的非共价相互作用。此前对该系统中对映选择性的结构起源了解甚少。通过系统的物理有机趋势分析探究了催化剂和底物取代基的影响。确定了底物与催化剂之间控制对映选择性的合理相互作用，并通过实验得到了支持，这表明这种方法可以提供一种利用机理见解来优化催化剂设计的有效手段。
