Complexity-Building Approaches in Photoassisted Synthesis of Heterocycles

University Of Denver, Denver CO

Investigators

Abstract

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.