Developing Asymmetric Catalysts Using Modular Ligands

在这个由化学系化学合成计划资助的项目中，犹他大学化学系的马修·西格曼教授将探索更有效地优化各种对映选择性反应的技术开发。传统上，催化剂优化仍然主要是经验性的，其中需要对大量的配体进行评估，这些配体通常在结构上是无关的，以开发成熟的手性催化剂。为了克服这一障碍，西格曼教授将使用模块化配体，它允许对配体结构进行系统的改变，并结合物理有机机械工具(即Hammett电子参数和Charton/Taft空间参数)和设计实验统计方法，以促进催化剂的设计和优化。这项工作既可以为具有挑战性的、合成上有用的转化提供新的对映选择性催化剂，也可以为机械分析和催化剂优化开发新的方法。这些领域的成功将对任何需要分子合成和催化剂优化的活动领域产生影响，例如制药、化工、农业及其生物和化学研究活动。此外，该项目将为学生提供培训，从本科前到博士后，包括那些在科学界历来代表性不足的学生。

In this project funded by the Chemical Synthesis Program of the Chemistry Division, Professor Matthew Sigman of the Department of Chemistry at the University of Utah will explore the development of techniques to more efficiently optimize diverse enantioselective reactions. Classically, catalyst optimization has remained mainly empirical, wherein evaluation of a significant number of ligands, often structurally unrelated, is required to develop a mature chiral catalyst. To overcome this impediment, Professor Sigman will use modular ligands, which permit the introduction of systematic changes to the ligand structure, in combination with physical organic mechanistic tools (i.e., Hammett electronic parameters and Charton/Taft steric parameters) and design of experimental statistical approaches to facilitate catalyst design and optimization. This work could lead to both new enantioselective catalysts for challenging, synthetically useful transformations and the development of new approaches for mechanistic analysis and catalyst optimization. Success in each of these areas will have an impact on any area of activity in which the synthesis of molecules and catalyst optimization is needed, such as the pharmaceutical, chemical, agricultural industries and their biological and chemical research activities. In addition, this project will provide training of students, from pre-undergraduate to post-doctoral, including those from groups historically underrepresented in the sciences.