Enabling FE-catalyzed cross-electrophile… (F32GM163427) – Project Grant to University Of California, LOS…

Abstract: Transition metal-catalyzed, carbon-carbon bond-forming reactions are ubiquitous in the synthesis of pharmaceuticals and agrochemicals. FE-catalyzed multicomponent cross-couplings (mccs) are a powerful strategy to rapidly build molecular complexity via the construction of a new c(SP2)-c(SP3) and c(SP3)-c(SP3) bond in one pot. However, these transformations are currently limited by the need for slow addition of the nucleophile via syringe pump. Cross-electrophile couplings (xecs) are a powerful strategy for forging bonds, commonly between two organohalides by the aid of a catalyst reduction event. However, FE catalysis is much less developed in xecs compared to other first-row transition metals like NI. IT is an attractive choice of metal catalyst as IT is the most abundant transition metal on earth. However, compared to more commonly used metals in traditional cross-couplings (e.g. PD and NI), the mechanisms of FE-catalyzed cross-couplings are comparatively not well understood. Additionally, the propensity for open-shell, paramagnetic intermediates in FE catalysis and fast reaction rates makes mechanistic study more challenging without a highly interdisciplinary approach involving computation and advanced inorganic spectroscopic methods. The advancement of PD and NI catalysis in the past 50 years is largely due to efforts towards metal-specific ancillary ligand design to support precatalysts and catalytic intermediates through various oxidation states and preventing catalyst decomposition. Thus, we hypothesize that a dedicated analysis of ligand features on catalyst performance, mechanistic study, and subsequent ligand design for FE would expand the applicability of FE-catalyzed methodologies for organic synthesis. This work seeks to develop a general method for FE-catalyzed cross-electrophile multicomponent cross-couplings (XE-mccs) using high-throughput experimentation (HTE) and machine learning to select a general substrate scope, identify general reaction conditions, and screen a range of bidentate ligands based on emerging "privileged" ligand scaffolds in FE catalysis (aim 1). Spectroscopic and crystallographic mechanistic studies will identify and characterize catalytic intermediates and off-cycle species, which will aid in the computational elucidation of the catalytic cycle. The identification of key intermediates will directly inform molecular modeling, which will be used to extract key ligand parameters for multivariate linear regression models to correlate ligand features and reaction outcome. These models will inform ligand selection/design for XE-mccs as well as other FE-catalyzed reactions proceeding through similar intermediates (aim 2). Finally, the development of a dynamic, substrate-responsive machine learning algorithm will enable streamlined optimization of reaction conditions and catalysts for FE-catalyzed XE-mccs on untested, medicinally relevant substrates (aim 3). This work will be enabled via a highly multidisciplinary approach to catalytic reaction development, as well as both intra- and inter-departmental collaborations at ucla and the university of oxford, respectively.

Enabling FE-catalyzed cross-electrophile multicomponent cross-couplings via data science driven ligand design

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