Complexity-Building Approaches in Photoassisted Synthesis of Heterocycles

光辅助合成杂环的复杂性构建方法

• 批准号: 2247411
• 负责人: Andrei Kutateladze
• 金额: $ 57.5万
• 依托单位: University of Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247411&HistoricalAwards=false
• 关键词: Complexity; Building; Approaches; Photoassisted; Synthesis

With the support of the Chemical Synthesis Program in the Division of Chemistry, Professor Andrei Kutateladze of University of Denver is studying the development of new molecular complexity-building methods for the synthesis of organic compounds that possess potentially useful properties. The major thrust of this research is based on photoassisted approaches, i.e. using light as a readily available energy source, allowing for rapid access to complex molecular architectures. Besides the fundamental importance of learning about the interactions of light and matter in the context of synthetic chemistry, the targeted complex heterocycles, i.e. molecules that contain nitrogen, sulfur, or oxygen atoms as a part of a ring, are intentionally designed to resemble alkaloids, an important class of natural products that may possess useful therapeutic properties. Importantly, this award will also support research experiences for undergraduate and graduate students, including those from historically underrepresented groups in science, thereby working toward inclusive excellence in the field of organic chemistry. This research aims to further expand the synthetic photochemistry toolbox, specifically to further develop excited state intramolecular proton transfer (ESIPT)-based photoassisted synthetic reaction cascades. For example, the Kutateladze group is targeting hydrogen atom transfer (HAT) in triplet states, designed to achieve large step-normalized complexity increases in the synthesis of complex heterocyclic molecular architectures. This work will also include a vertical growth in complexity through post-photochemical transformations. Reaction mechanisms and the stereochemistry of these ESIPT-triggered cascades will be investigated to better understand how these transformations achieve their stereo- and regiochemical outcomes. The experimental work will be augmented by computations that have been designed to facilitate solution structure elucidation of the complex heterocyclic products by nuclear magnetic resonance (NMR) spectroscopy. The newly integrated machine learning-augmented density functional theory (DFT) method for fast and accurate calculations of the NMR parameters will be expanded to explicitly account for the effect of solvents on the predicted spectra. This sub-theme of the proposed research is critical for the success of this project because of the structural complexity of the target molecules. Moreover, further development of this method will likely have broader implications for the structure elucidation of complex organic molecules, in general.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.

在化学合成计划中的支持下，丹佛大学的Andrei Kutateladze教授正在研究合成具有潜在有用特性的有机化合物的新分子复杂性构建方法的发展。这项研究的主要目的是基于光助手方法，即使用光作为易于使用的能源，从而可以快速获取复杂的分子体系结构。除了在合成化学的背景下学习光与物质的相互作用的基本重要性外，有针对性的复杂杂环，即包含氮，硫或氧原子的分子作为环的一部分，是故意设计的，它们是故意设计的，旨在类似于碱性产品的重要类别，具有有用的自然产物。重要的是，该奖项还将支持本科生和研究生的研究经验，包括历史上代表性不足的科学群体的研究经验，从而致力于有机化学领域的包容性卓越。这项研究旨在进一步扩展合成光化学工具箱，特别是为了进一步发展激发态分子内质子转移（ESIPT）基于基于光的光伴侣合成反应级联。例如，Kutateladze组靶向三胞胎状态下的氢原子转移（HAT），旨在实现复杂杂环分子架构的合成中大量的步差要复杂性的提高。这项工作还将包括通过光学化学转化的复杂性垂直增长。将研究这些ESIPT触发的级联反应机理和立体化学，以更好地了解这些转换如何实现其立体和条款化学结果。通过旨在通过核磁共振（NMR）光谱阐明溶液结构阐明溶液结构的计算将增强实验工作。将扩展到快速准确计算NMR参数的新集成机器学习增强的密度功能理论（DFT）方法，以明确说明溶剂对预测光谱的影响。拟议研究的这一子主题对于该项目的成功至关重要，因为目标分子的结构复杂性。此外，这种方法的进一步发展可能会对复杂有机分子的结构阐明具有更广泛的影响。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和更广泛影响的评估评估标准的评估值得支持的。