Saturday, September 7, 2024
9/7/2024
Project Abstract The goal of this project is to develop new understanding and predictive models for the formation, reactivity, and selectivity of organic radical and diradical intermediates. Triplet diradicals can undergo a variety of transformations that are not accessible in the singlet ground state. However, developing efficient photocatalytic triplet energy transfer processes, particularly in an enantioselective fashion, remains an enduring goal. We will show that computational approaches can be leveraged to develop general principles for substrate and sensitizer design to harness triplet-state reactivity. We will use computation to target the mechanism-guided discovery of unexplored reactivity in the triplet state, such as homolytic aromatic substitution, and the design of chiral Lewis acids to promote asymmetric photocatalytic cyclizations. We will also develop a qualitative and quantitative understanding of the factors controlling the reaction rates and site selectivities of radical homolytic substitution, such as hydrogen and halogen atom transfer reactions. These conceptual insights will be used to rationalize experimental observations and underpin the development of new radical reagents for site-selective C(sp3)-H chlorination. The development of quantitative models, aided by new physical-organic parameters, can be used to accelerate this process. Machine learning models grounded in mechanistic understanding will provide new tools to parametrize substrates and reagents to accelerate reaction discovery and optimization. We will employ this strategy to predict the site-selectivity of P450 oxidation small molecules and to establish general workflows to predict the metabolic degradation pathways.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
