Frustrated Radical Pairs in Organic Synthesis.

Frustrated radical pairs (FRPs) describe the phenomenon that two distinct radicals─which would otherwise annihilate each other to form a closed-shell covalent adduct─can coexist in solution, owing to steric repulsion or weak bonding association. FRPs are typically formed via spontaneous single-electron transfer between two sterically encumbered precursors─an oxidant and a reductant─under ambient conditions. The two components of a FRP exhibit orthogonal chemical properties and can often act in cooperativity to achieve interesting radical reactivities. Initially observed in the study of traditional frustrated Lewis pairs, FRPs have recently been shown to be capable of homolytically activating various chemical bonds. In this Perspective, we will discuss the discovery of FRPs, their fundamental reactivity in chemical bond activation, and recent developments of their use in synthetic organic chemistry, including in C-H bond functionalization. We anticipate that FRPs will provide new reaction strategies for solving challenging problems in modern organic synthesis.

Regioselective aliphatic C-H functionalization using frustrated radical pairs.

Frustrated Lewis pairs (FLPs) are well documented for the activation of small molecules such as dihydrogen and carbon dioxide1-4. Although canonical FLP chemistry is heterolytic in nature, recent work has shown that certain FLPs can undergo single-electron transfer to afford radical pairs5. Owing to steric encumbrance and/or weak bonding association, these radicals do not annihilate one another, and they have thus been named frustrated radical pairs (FRPs). Notable preliminary results suggest that FRPs may be useful reagents in chemical synthesis6-8, although their applications remain limited. Here we demonstrate that the functionalization of C(sp3)-H bonds can be accomplished using a class of FRPs generated from disilazide donors and an N-oxoammonium acceptor. Together, these species undergo single-electron transfer to generate a transient and persistent radical pair capable of cleaving unactivated C-H bonds to furnish aminoxylated products. By tuning the structure of the donor, it is possible to control regioselectivity and tailor reactivity towards tertiary, secondary or primary C-H bonds. Mechanistic studies lend strong support for the formation and involvement of radical pairs in the target reaction.

Deep Electroreductive Chemistry: Harnessing Carbon- and Silicon-based Reactive Intermediates in Organic Synthesis.

This Viewpoint outlines our recent contribution in electroreductive synthesis. Specifically, we leveraged deeply reducing potentials provided by electrochemistry to generate radical and anionic intermediates from readily available alkyl halides and chlorosilanes. Harnessing the distinct reactivities of radicals and anions, we have achieved several challenging transformations to construct C-C, C-Si, and Si-Si bonds. We highlight the mechanistic design principle that underpinned the development of each transformation and provide a view forward on future opportunities in growing area of reductive electrosynthesis.

Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources.

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)-C(sp3) bond formation. While many of these methods typically employ aryl bromides as the C(sp2) coupling partner, a variety of aliphatic radical sources have been investigated. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because nonstandardized sets of aryl bromides are used in scope evaluation. Herein, we report a Ni/photoredox-catalyzed (deutero)methylation and alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. Reaction development, mechanistic studies, and late-stage derivatization of a biologically relevant aryl chloride, fenofibrate, are presented. Then, we describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing scope examples from published Ni/photoredox methods on this same chemical space, we identify areas of sparse coverage and high versus low average yields, enabling comparisons between prior art and this new method. Additionally, we demonstrate that the systematically selected scope of aryl bromides can be used to quantify population-wide reactivity trends and reveal sources of possible functional group incompatibility with supervised machine learning.

Predicting Reaction Yields via Supervised Learning.

Numerous disciplines, such as image recognition and language translation, have been revolutionized by using machine learning (ML) to leverage big data. In organic synthesis, providing accurate chemical reactivity predictions with supervised ML could assist chemists with reaction prediction, optimization, and mechanistic interrogation.To apply supervised ML to chemical reactions, one needs to define the object of prediction (e.g., yield, enantioselectivity, solubility, or a recommendation) and represent reactions with descriptive data. Our group's effort has focused on representing chemical reactions using DFT-derived physical features of the reacting molecules and conditions, which serve as features for building supervised ML models.In this Account, we present a review and perspective on three studies conducted by our group where ML models have been employed to predict reaction yield. First, we focus on a small reaction data set where 16 phosphine ligands were evaluated in a single Ni-catalyzed Suzuki-Miyaura cross-coupling reaction, and the reaction yield was modeled with linear regression. In this setting, where the regression complexity is strongly limited by the amount of available data, we emphasize the importance of identifying single features that are directly relevant to reactivity. Next, we focus on models trained on two larger data sets obtained with high-throughput experimentation (HTE). With hundreds to thousands of reactions available, more complex models can be explored, for example, models that algorithmically perform feature selection from a broad set of candidate features. We examine how a variety of ML algorithms model these data sets and how well these models generalize to out-of-sample substrates. Specifically, we compare the ML models that use DFT-based featurization to a baseline model that is obtained with features that carry no physical information, that is, random features, and to a naive non-ML model that averages yields of reactions that share the same conditions and substrate combinations. We find that for only one of the two data sets, DFT-based featurization leads to a significant, although moderate, out-of-sample prediction improvement. The source of this improvement was further isolated to specific features which allowed us to formulate a testable mechanistic hypothesis that was validated experimentally. Finally, we offer remarks on supervised ML model building on HTE data sets focusing on algorithmic improvements in model training.Statistical methods in chemistry have a rich history, but only recently has ML gained widespread attention in reaction development. As the untapped potential of ML is explored, novel tools are likely to arise from future research. Our studies suggest that supervised ML can lead to improved predictions of reaction yield over simpler modeling methods and facilitate mechanistic understanding of reaction dynamics. However, further research and development is required to establish ML as an indispensable tool in reactivity modeling.

Direct Use of Carboxylic Acids in the Photocatalytic Hydroacylation of Styrenes To Generate Dialkyl Ketones.

A general protocol for the hydroacylation of styrenes from aliphatic carboxylic acids is reported. These reactions proceed via ß-scission of a phosphoranyl radical that is accessed by photoredox catalysis, followed by addition of the resulting acyl radical to the styrenyl olefin. We show that phosphine tunability is critical for efficient intermolecular coupling due to competitive quenching of the photocatalyst by the olefin. Primary, secondary, and structurally rigid tertiary carboxylic acids all generate valuable unsymmetrical dialkyl ketones.

Direct C-C Bond Formation from Alkanes Using Ni-Photoredox Catalysis.

A method for direct cross coupling between unactivated C(sp3)-H bonds and chloroformates has been accomplished via nickel and photoredox catalysis. A diverse range of feedstock chemicals, such as (a)cyclic alkanes and toluenes, along with late-stage intermediates, undergo intermolecular C-C bond formation to afford esters under mild conditions using only 3 equiv of the C-H partner. Site selectivity is predictable according to bond strength and polarity trends that are consistent with the intermediacy of a chlorine radical as the hydrogen atom-abstracting species.

Asymmetric Organocatalytic Reductive Coupling Reactions between Benzylidene Pyruvates and Aldehydes.

An organocatalytic three-component reductive coupling reaction between dimethyl phosphite, benzylidene pyruvates, and aldehydes is reported. A chiral triaryliminophosphorane catalyst promotes Pudovik addition, which is followed by phospha-Brook rearrangement to transiently generate enolates that are trapped stereoselectively by aldehydes. This reductive coupling provides vicinal polyfunctionalized stereocenters from readily available prochiral starting materials with excellent diastereoselectivity, enantioselectivity, and yield.