Discovery and Optimization of Enantioselective Catalysts Guided by Informatics and Machine Learning

信息学和机器学习引导的对映选择性催化剂的发现和优化

• 批准号: 2154237
• 负责人: Scott Denmark
• 金额: $ 60万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154237&HistoricalAwards=false
• 关键词: Discovery; Optimization; Enantioselective; Catalysts; Guided

With support from the Chemical Catalysis Program in the NSF Division of Chemistry, Professor Scott E. Denmark of the Department of Chemistry at the University of Illinois, Urbana-Champaign (UIUC) and his team are working to develop a new approach to the design of catalytic processes that combines the creative power of diversity-oriented synthesis with the computational power of informatics. The foundational requirements for this program are the invention and implementation of high-resolution chemical descriptors that are able to accurately reflect the chemical properties of molecules in computationally readable form. The development of a truly general, computationally guided workflow for the optimization of molecular function could have broad scientific impact by providing the experimentalist with better tools to design new compounds with desirable chemical properties from target/therapeutic selectivity to optical and materials properties. Of paramount interest here is the discovery and optimization of catalysts for industrially relevant chemical processes. The planned research activities planned are ideal for the intellectual and practical training of students at the interface of preparative organic chemistry and data science. The unifying theme of this activity is the invention of new chemical reactions that challenge current thinking. In the context of educational outreach, funds are requested to support an undergraduate intern each of the three years under the auspices of the St. Elmo Brady Summer Research Scholars Program at UIUC. This program is a 10-week summer research experience to increase the percentages of students from underserved groups in the sciences. Professor Denmark and his UIUC team are will developing a research program that combines computational analysis with experimentation, in an iterative fashion, namely: (1) in silico generation of a large library of hypothetical catalyst structures, based on a given scaffold, followed by calculation of descriptors of each library member, (2) diversity analysis to generate a representative “training set”, (3) synthesis of the training set, (4) evaluation of the training set in a given reaction, (5) development and validation of a mathematical model of that correlates empirical output with molecular properties (6) application of that model to the virtual library of catalysts, (7) synthesis and evaluation of best predicted catalysts, and (8) the repetition of steps 4-7 until desired output is achieved. One of the most powerful features of this approach is that the training set is applicable to a wide variety of reactions that are susceptible to catalysis by that scaffold. Accordingly, all three objectives encompass the established chemoinformatic/experimental workflow. Within each objective, the training set generated for each scaffold will be evaluated for the optimization of several chemical reactions. Because the catalyst selection process is performed using catalyst descriptors alone, this process is reaction- and mechanism-agnostic, and chosen catalyst training sets can be used to evaluate many transformations. Each objective involves the generation, synthesis, evaluation and optimization a ligand or catalyst: Objective 1 – Brønsted acid catalysts for atropo-selective cyclohalogenation; Objective 2 – Enantioselective oxidative nitroso-ene reactions and reductive Heck reactions and Objective 3 – Enantioselective epoxide and aziridine desymmetrization under organometallic catalysis.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.

在美国国家科学基金会化学部化学催化项目的支持下，伊利诺伊大学厄巴纳-香槟分校（UIUC）化学系的丹麦和他的团队正在努力开发一种新的催化过程设计方法，将多样性导向合成的创造力与信息学的计算能力相结合。该计划的基本要求是发明和实施高分辨率化学描述符，这些描述符能够以计算可读的形式准确反映分子的化学性质。开发一个真正通用的，计算指导的工作流程优化分子功能可以有广泛的科学影响，提供更好的工具，实验设计新的化合物具有理想的化学性质，从目标/治疗选择性的光学和材料性能。 这里最重要的兴趣是发现和优化工业相关化学过程的催化剂。计划的研究活动是理想的学生在制备有机化学和数据科学的接口的智力和实践训练。这项活动的统一主题是挑战当前思维的新化学反应的发明。在教育推广方面，要求提供资金，以支持在圣埃尔莫布雷迪夏季研究学者方案的主持下，在UIUC的三年每一个本科实习生。该计划是一个为期10周的夏季研究经验，以提高学生在科学服务不足的群体的比例。丹麦教授和他的UIUC团队将开发一个研究计划，结合计算分析与实验，在迭代的方式，即：（1）基于给定的支架，在计算机上生成假设的催化剂结构的大型库，随后计算每个库成员的描述符，（2）多样性分析以生成代表性的“训练集”，（3）训练集的合成，（4）给定反应中训练集的评估，（5）将经验输出与分子性质相关联的数学模型的开发和验证，（6）将该模型应用于催化剂的虚拟库，（7）最佳预测催化剂的合成和评估，以及（8）重复步骤4-7直到获得所需的输出。这种方法最强大的功能之一是，训练集适用于各种各样的反应，这些反应对该支架的催化敏感。因此，所有三个目标都包括已建立的化学信息学/实验工作流程。在每个目标中，将评估为每个支架生成的训练集，以优化几个化学反应。因为催化剂选择过程是单独使用催化剂描述符进行的，所以该过程是反应和机理不可知的，并且所选择的催化剂训练集可以用于评估许多转化。每个目标都涉及配体或催化剂的产生、合成、评估和优化：目标1 -用于原子选择性环卤化的布朗斯台德酸催化剂;目的2 -对映选择性氧化亚硝基烯反应和还原Heck反应，目的3 -有机金属催化下的对映选择性环氧化物和氮丙啶去对称化。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。