Computational Prediction of Enantioselectivity in Metal-Catalyzed Reactions

许多分子是手性的，这意味着它们可以以两种镜像形式存在，类似于右手和左手。通常，右手和左手形式与生物系统的相互作用是不同的，有时会产生深远的后果。一方面可能是有益的，而另一方面也可能是有益的，没有影响，或有害的。因此，手性性质是设计和制造分子（如药物）时极其重要的考虑因素。最有效的手性分子合成使用的催化剂也是手性的。 不幸的是，手性催化剂的合成通常需要低效的试错方法。在这个项目中，圣母大学的Wiest和Helquist教授利用计算化学、机器学习和有机化学来开发高效、快速和准确的方法来预测如何制造手性分子。在与一家大型制药公司的合作中，这些方法正在改善对制药行业重要分子的获取。这种计算驱动合成的新方法以及与行业的密切长期合作为学生创造了独特的体验。这些学生参加瑞典阿斯利康的工业实习和跨学科培训。 这两位高级研究人员正在圣母大学和中西部积极参与扩大参与倡议。这些活动包括通过建筑桥梁计划和高中生暑期研究指导来自代表性不足群体的本科生。在化学部化学催化计划的资助下，圣母大学的Wiest博士和Helquist博士继续开发量子引导分子力学（Q2MM）方法，用于过渡态力场（TSFF）的自动参数化，然后用于对映选择性催化剂（CatVS）的高通量虚拟筛选。 一些方法的改进，如机器学习，新的接口，和自动化参数化程序，和TSFFs的新的反应的利益为合成有机社区的发展。在应用方面，新方法用于氢原子转移反应的新型催化剂的快速原型制作，以及该团队开发的新型交叉偶联反应的对映选择性版本的虚拟库的高通量筛选。Q2MM和CatVS代码以及经验证的力场继续通过github.com/q2mm免费提供给科学界。计算和实验相结合的方法，再加上与阿斯利康的密切合作，为本科生和研究生提供了一个独特的跨学科培训环境。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many molecules are chiral, which means that they can exist in two forms that are mirror images, analogous to right and left hands. Very often the right and left hand forms interact differently with biological systems, sometimes with profound consequences. One hand may be beneficial while the other hand may also be beneficial, have no effect, or be harmful. As a result, the handedness property is an extremely important consideration in the design and manufacture of molecules such as pharmaceuticals. The most efficient syntheses of chiral molecules use catalysts that are also chiral. Unfortunately, the synthesis of chiral catalysts often requires inefficient trial and error approaches. In this project, Professors Wiest and Helquist at the University of Notre Dame employ computational chemistry, machine learning, and organic chemistry to develop efficient, fast, and accurate methods to predict how to make chiral molecules. In collaboration with a major pharmaceutical company, these methods are improving access to molecules of importance to the pharmaceutical industry. This new approach to computationally-driven synthesis and the close, long-term collaboration with industry creates unique experiences for their students. These students participate in industrial internships and interdisciplinary training at AstraZeneca in Sweden. The two senior researchers are actively engaging in broadening participation initiatives at the University of Notre Dame and in the Midwest. These activities include mentoring undergraduate students from underrepresented groups through the Building Bridges program and summer research for high school students. With funding from the Chemical Catalysis Program of the Chemistry Division, Dr. Wiest and Dr. Helquist of the University of Notre Dame continue the development of the quantum guided molecular mechanics (Q2MM) method for the automated parameterization of transition state force fields (TSFFs) which are then used for the high-throughput virtual screening of enantioselective catalysts (CatVS). A number of methodological improvements, such as machine learning, new interfaces, and automated parameterization procedures, and TSFFs for new reactions of interests for the synthetic organic community are developed. On the application side, the new methods are used for rapid prototyping of novel catalysts for hydrogen atom transfer reactions and for high throughput screening of virtual libraries for an enantioselective version of a novel cross-coupling reaction developed by the team. The Q2MM and CatVS code, as well as the validated force fields continue to be available to the scientific community free of charge via github.com/q2mm. The combined computational and experimental approach, combined with the close collaboration with AstraZeneca, provides a unique interdisciplinary training environment for the undergraduate and graduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.