Discovering Modular Catalysts for Selective Synthesis with Computation

通过计算发现用于选择性合成的模块化催化剂

• 批准号: 2400056
• 负责人: Robert Paton
• 金额: $ 52.92万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2400056&HistoricalAwards=false
• 关键词: Discovering; Modular; Catalysts; Selective; Synthesis

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Robert Paton of Colorado State University (CSU) is studying new computational models and tools that will guide the discovery of synthetic methods to access chemical motifs commonly found in medicines. The Paton laboratory aims to develop new methods of theory and simulation to accelerate the discovery of new catalysts and new reactions for the highly selective construction of drug-like organic molecules. Automated workflows will be created to accelerate catalyst discovery by identifying modular structures that can be assembled from smaller molecular building blocks. This fundamental science will be enabled by new mechanistic collaborations with experimentalists that broaden the horizons of the participating scientists at Colorado State University. The research team, which will include undergraduate and postgraduate researchers, will learn a diverse array of skills well-aligned with the modern STEM (science, technology, engineering and mathematics) workplace. The teams will conduct outreach efforts to create video content introducing the people and research involved in this project. The team will engage the broader public through outreach events in Colorado, such as CSU Speaks.Under this award, Professor Robert Paton and his research team at Colorado State University will develop new computational workflows for the discovery and optimization of highly selective catalysts. This work seeks to address the modularity and tunability of three distinct catalyst and ligand classes: hydrogen-bond donors based on urea and peptidic motifs, phosphite and phosphoramidite ligands used in transition metal catalysis, and trialkyloxonium ions. In each application area, the Paton laboratory will develop new mechanistic and statistical models grounded in physical-organic chemistry in collaboration with synthetic experts. The research team will establish high-throughput computational workflows to obtain steric and electronic descriptors for large catalyst and ligand libraries, and multistep protocols combining cheminformatics, semi-empirical calculations, and quantum chemical-based analyses. The projected outcomes of these research approaches include new mechanistic understanding in three important areas of catalysis, a broader understanding of the principles of structure-based catalyst design using high- and low-throughput techniques, and experimentally validated structures that have been identified computationally. A central principle underlying this program is that conceptual insights from mechanistic studies and transition state modeling have the potential to impact the development of new quantitative descriptors and novel concepts for reaction design in a broad sense. Translating a detailed theoretical understanding into practical computational tools has the potential to accelerate reagent/catalyst design in increasingly complex settings.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.

在化学催化计划的支持下，科罗拉多州立大学（CSU）的罗伯特·帕顿（Robert Paton）教授正在研究新的计算模型和工具，这些模型和工具将指导发现合成方法以访问药物中常见的化学图案。 Paton实验室旨在开发新的理论和模拟方法，以加速新的催化剂和新反应，以高度选择性地构建类似药物的有机分子。通过识别可以从较小的分子构建块组装的模块化结构，将创建自动化工作流程以加速催化剂发现。这项基本科学将通过与实验者的新机械合作来启动，从而扩大了科罗拉多州立大学参与科学家的视野。该研究团队将包括本科和研究生研究人员，将学习与现代STEM（科学，技术，工程和数学）工作场所合格的各种技能。团队将进行外展工作，以创建视频内容，介绍该项目的人员和研究。该团队将通过科罗拉多州的外展活动（例如CSU讲话）与更广泛的公众吸引。这项工作旨在解决三种不同的催化剂和配体类别的模块化和可调节性：基于尿素和肽基序的氢键供体，用于过渡金属催化的磷酸盐和磷酸胺配体，以及试验性金属催化剂。在每个应用领域，Paton实验室将与合成专家合作开发以物理有机化学为基础的新机械和统计模型。研究团队将建立高通量计算工作流程，以获取用于大型催化剂和配体库的空间和电子描述，以及结合化学形式，半经验计算以及基于量子化学化学化学的分析的多步协议。这些研究方法的预期结果包括在催化的三个重要领域中进行的新机械理解，对使用高通量和低通量技术的基于结构的催化剂设计原理的更广泛理解，以及经过计算鉴定的实验验证的结构。该计划的基本原则是，机械研究和过渡状态建模的概念见解有可能影响新的定量描述源的发展，并在广义上影响反应设计的新颖概念。将详细的理论理解转化为实用的计算工具，有可能在日益复杂的环境中加速试剂/催化剂设计。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来获得支持的。