Computational Prediction of Enantioselectivity in Metal-Catalyzed Reactions

在这个由化学部化学催化计划资助的项目中，圣母大学的Paul Helquist和Olaf Wiest将开发计算方法来预测手性催化剂和底物的任何组合的氢化对映选择性。 该方法是使用量子力学方法计算给定过渡金属催化加氢反应的相关过渡结构;使用量子力学指导的分子力学（Q2 MM）方法参数化和验证基态和过渡态力场（TSFF）;使用TSFF计算相关非对映体过渡结构的活化自由能差异;使用力场快速预测虚拟配体库的对映体选择性;最后使用实验方法验证顶级配体候选物。 更广泛的影响包括培训本科生和研究生，通过夏季研究实验室经验向当地高中生推广，广泛传播研究成果和Q2 MM软件，这是一个关于催化对日常生活重要性的普通公众科学展览，以及预测催化反应的立体化学结果而无需进行实验筛选的能力的科学和技术影响。这项工作将加深我们对催化剂如何使一个分子选择性地成为一对镜像分子的理解，并提高我们使用计算方法预测这种催化反应结果的能力。 这些知识将有利于精细化工和制药行业，并可能导致更快，更有效的方法来合成药物和其他类型的生物活性化合物。

In this project funded by the Chemical Catalysis Program of the Chemistry Division, Paul Helquist and Olaf Wiest of the University of Notre Dame will develop computational methods to predict hydrogenation enantioselectivity of any combination of chiral catalyst plus substrate. The approach is to calculate the pertinent transition structures for a given transition metal-catalyzed hydrogenation reaction using quantum mechanical methods; to parameterize and validate the ground and transition state force fields (TSFF) using the quantum mechanics-guided molecular mechanics (Q2MM) approach; to calculate the differences in free energy of activation of the relevant diastereomeric transition structures using the TSFFs; to use the force fields for rapid prediction of enantioselectivities for virtual ligand libraries; and finally to validate the top ligand candidates using experimental methods. The broader impacts involve training undergraduate and graduate students, outreach to local high school students through a summer research laboratory experience, broad dissemination of research results and Q2MM software, a general public science exhibit on the importance of catalysis on everyday life, and the scientific and technological impacts of the ability to predict stereochemical outcomes of catalytic reactions without having to perform experimental screening.This work will deepen our understanding of how catalysts can make one molecule selectively of a pair that are mirror images and advance our ability to predict the outcomes of such catalytic reactions using computational methods. Such knowledge would benefit the fine chemical and pharmaceutical industries and potentially lead to faster, more efficient methods to synthesize drugs and other types of bioactive compounds.