Rapid, Homogenous, B-Alkyl Suzuki-Miyaura Cross-Coupling of Boronic Esters.

A rapid, anhydrous Suzuki-Miyaura cross-coupling of alkylboronic esters with aryl halides is described. Parallel experimentation revealed that the combination of AntPhos, an oxaphosphole ligand, neopentyldiol alkylboronic esters, and potassium trimethylsilanolate (TMSOK) enables successful cross-coupling. In general, reactions proceed in under 1 h with good yields and high linear/branched (l/b) selectivities. Crucially, two literature examples which previously took >20 h to reach completion were accomplished in a fraction of the time with the method described herein. Mechanistic studies revealed that the reaction proceeds through a stereoretentive pathway and identified the boronic ester skeleton as a predominant pathway for deleterious protodehalogenation.

Machine Learning to Develop Peptide Catalysts-Successes, Limitations, and Opportunities.

Peptides have been established as modular catalysts for various transformations. Still, the vast number of potential amino acid building blocks renders the identification of peptides with desired catalytic activity challenging. Here, we develop a machine-learning workflow for the optimization of peptide catalysts. First-in a hypothetical competition-we challenged our workflow to identify peptide catalysts for the conjugate addition reaction of aldehydes to nitroolefins and compared the performance of the predicted structures with those optimized in our laboratory. On the basis of the positive results, we established a universal training set (UTS) containing 161 catalysts to sample an in silico library of ∼30,000 tripeptide members. Finally, we challenged our machine learning strategy to identify a member of the library as a stereoselective catalyst for an annulation reaction that has not been catalyzed by a peptide thus far. We conclude with a comparison of data-driven versus expert-knowledge-guided peptide catalyst optimization.

Effects of Ring Size and Steric Encumbrance on Boron-to-Palladium Transmetalation from Arylboronic Esters.

The structure of the diol from which an arylboronic ester is derived dramatically influences the rate of transmetalation in the Suzuki-Miyaura cross-coupling reaction. Some esters undergo transmetalation more than 20 times faster than the parent arylboronic acid. Herein, investigations into the influence of arylboronic ester ring size and steric properties on the mechanism of transmetalation in the Suzuki-Miyaura reaction are described. Both factors impact the propensity of an arylboronic ester to bind to a dimeric palladium hydroxide complex. The reaction of hindered arylboronic esters derived from 1,2-diols (1,3,2-dioxaborolanes) with palladium hydroxide dimers to form a complex incorporating a Pd-O-B linkage is thermodynamically favorable, but the barrier to coordination is often higher than the barrier to arene transfer. In contrast, the analogous reaction between arylboronic esters derived from 1,3-diols (1,3,2-dioxaborinanes) and palladium hydroxide dimers is thermodynamically unfavorable, as 1,3,2-dioxaborinanes exhibit decreased electrophilicity compared to esters derived from 1,2- or 1,4-diols. These factors also influence the barrier of the arene transfer step, and in many cases, arylboronic esters that do not easily form Pd-O-B linked complexes undergo transmetalation faster than those that do because of hyperconjugative stabilization of the arene transfer transition state.

Albert Eschenmoser (1925-2023).

Organic chemist who demystified the logic of natural product structures.

A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C-N couplings.

Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)-catalyzed carbon-nitrogen (C-N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C-N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows.

High-Level Data Fusion Enables the Chemoinformatically Guided Discovery of Chiral Disulfonimide Catalysts for Atropselective Iodination of 2-Amino-6-arylpyridines.

The atropselective iodination of 2-amino-6-arylpyridines catalyzed by chiral disulfonimides (DSIs) is described. Key to the development of this transformation was the use of a chemoinformatically guided workflow for the curation of a structurally diverse training set of DSI catalysts. Utilization of this catalyst training set in the atropselective iodination across a variety 2-aminopyridine substrates allowed for the recommendation of statistically higher-performing DSIs for this reaction. Data Fusion techniques were implemented to successfully predict the performance of catalysts when classical linear regression analysis failed to provide suitable models. This effort identified a privileged class of 3,3'-alkynyl-DSI catalysts which were effective in catalyzing the iodination of a variety of 2-amino-6-arylpyridines with high stereoselectivity and generality. Subsequent preparative-scale demonstrations highlighted the utility of this reaction by providing iodinated pyridines >90:10 er and in good chemical yield.

Potassium Trimethylsilanolate-Promoted, Anhydrous Suzuki-Miyaura Cross-Coupling Reaction Proceeds via the "Boronate Mechanism": Evidence for the Alternative Fork in the Trail.

Previous studies have shown that the critical transmetalation step in the Suzuki-Miyaura cross-coupling proceeds through a mechanism wherein an arylpalladium hydroxide complex reacts with an aryl boronic acid, termed the oxo-palladium pathway. Moreover, these same studies have established that the reaction between an aryl boronate and an arylpalladium halide complex (the boronate pathway) is prohibitively slow. Herein, studies on isolated intermediates, along with kinetic analysis, have demonstrated that the Suzuki-Miyaura reaction promoted by potassium trimethylsilanolate (TMSOK) proceeds through the boronate pathway, in contrast with other, established systems. Furthermore, an unprecedented, binuclear palladium(I) complex containing a µ-phenyl bridging ligand was characterized by NMR spectroscopy, mass spectrometry, and computational methods. Density functional theory (DFT) calculations suggest that the binuclear complex exhibits an open-shell ground electronic state, and reaction kinetics implicate the complex in the catalytic cycle. These results expand the breadth of potential mechanisms by which the Suzuki-Miyaura reaction can occur, and the novel binuclear palladium complex discovered has broad implications for palladium-mediated cross-coupling reactions of aryl halides.

Enantioselective Inter- and Intramolecular Sulfenofunctionalization of Unactivated Cyclic and (Z)-Alkenes.

A method for the enantioselective, Lewis base-catalyzed sulfenofunctionalization of cyclic and (Z)-alkenes is reported. The intermediate thiiranium ion generated in the presence of a selenophosphoramide catalyst is intercepted by a variety of nucleophiles. A diverse array of inter- and intramolecular functionalizations proceed in high yield and good to high enantioselectivity (86:14-98:2 er). Prior experimental and computational studies indicated such enantiotopic face discrimination to be poor; however, the results disclosed herein remediate the previous findings. Control experiments were performed to investigate the different behavior of (Z)-alkenes and their more established (E)-counterparts.

Synthesis of Enantioenriched 3,4-Disubstituted Chromans through Lewis Base Catalyzed Carbosulfenylation.

A method for the catalytic, enantioselective, carbosulfenylation of alkenes to construct 3,4-disubstituted chromans is described. Alkene activation proceeds through the intermediacy of enantioenriched, configurationally stable thiiranium ions generated from catalytic, Lewis base activation of an electrophilic sulfenylating agent. The transformation affords difficult-to-generate, enantioenriched, 3,4-disubstituted chromans in moderate to high yields and excellent enantioselectivities. A variety of substituents are compatible including electronically diverse functional groups as well as several functional handles such as aryl halides, esters, anilines, and phenols. The resulting thioether moiety is amenable to a number of functional group manipulations and transformations. Notably, the pendant sulfide was successfully cleaved to furnish a free thiol which readily provides access to most sulfur-containing functional groups which are present in natural products and pharmaceuticals.

Lewis Base Catalyzed, Sulfenium Ion Initiated Enantioselective, Spiroketalization Cascade.

A Lewis base catalyzed, enantioselective sulfenocyclization of alkenes to afford [6,6]spiroketals has been developed. The method uses a chiral Lewis base catalyst with an electrophilic sulfur source to generate enantioenriched thiiranium ion with alkenes. Upon formation, the thiiranium ion is subsequently captured in a cascade-type reaction, wherein a ketone oxygen serves as the nucleophile to open the thiiranium ion and an alcohol provides the secondary cyclization to form biorelevant spiroketals. A variety of electron-rich and electron-neutral E-substituted styrenes form the desired spiroketals in good yields with excellent enantio- and diastereoselectivities. Alkyl-substituted and terminal alkenes participate in the cascade reaction, but with a limited scope compared to the styrenyl substrates. This method allows for rapid formation of highly substituted spiroketals in good yield and excellent enantioselectivity.

Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality.

Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past it is intrinsically limited and inefficient. To address this problem, our laboratory has developed a fully informatics guided workflow to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and optimization of any class of catalyst for any transformation. This approach is mechanistically agnostic, but also serves as a discovery platform to identify high performing catalysts that can be subsequently investigated with physical organic methods to identify the origins of selectivity.

Catalytic, Enantioselective Syn-Oxyamination of Alkenes.

The chemo-, regio-, diastereo-, and enantioselective 1,2-oxyamination of alkenes using selenium(II/IV) catalysis with a chiral diselenide catalyst is reported. This method uses N-tosylamides to generate oxazoline products that are useful both as protected 1,2-amino alcohol motifs and as chiral ligands. The reaction proceeds in good yields with excellent enantio- and diastereoselectivity for a variety of alkenes and pendant functional groups such as sulfonamides, alkyl halides, and glycol-protected ketones. Furthermore, the rapid generation of oxazoline products is demonstrated in the expeditious assembly of chiral PHOX ligands as well as diversely protected amino alcohols.

Heteroaryl-Heteroaryl, Suzuki-Miyaura, Anhydrous Cross-Coupling Reactions Enabled by Trimethyl Borate.

Reaction conditions have been developed for refractory heteroaryl-heteroaryl Suzuki-Miyaura cross-couplings. The reported method employs neopentyl heteroarylboronic esters as nucleophiles, heteroaryl bromides and chlorides as the electrophiles, and the soluble base potassium trimethylsilanolate (TMSOK) under anhydrous conditions. The addition of trimethyl borate enhances reaction rates by several mechanisms, including (1) solubilization of in situ-generated boronate complexes, (2) preventing catalyst poisoning by the heteroatomic units, and (3) buffering the inhibitory effect of excess TMSOK. The use of this method enables cross-coupling of diverse reaction partners including a broad range of π-rich and π-deficient heteroaryl boronic esters and heteroaryl bromides. Reactions proceed in good yields and short reaction times (3 h or less).

Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts.

Catalyst design in enantioselective catalysis has historically been driven by empiricism. In this endeavor, experimentalists attempt to qualitatively identify trends in structure that lead to a desired catalyst function. In this body of work, we lay the groundwork for an improved, alternative workflow that uses quantitative methods to inform decision making at every step of the process. At the outset, we define a library of synthetically accessible permutations of a catalyst scaffold with the philosophy that the library contains every potential catalyst we are willing to make. To represent these chiral molecules, we have developed general 3D representations, which can be calculated for tens of thousands of structures. This defines the total chemical space of a given catalyst scaffold; it is constructed on the basis of catalyst structure only without regard to a specific reaction or mechanism. As such, any algorithmic subset selection method, which is unsupervised (i.e., only considers catalyst structure), should provide an ideal initial screening set for any new reaction that can be catalyzed by that scaffold. Notably, because this design strategy, the same set of catalysts can be used for any reaction that can be catalyzed with that parent catalyst scaffold. These are tested experimentally, and statistical learning tools can be used to create a model relating catalyst structure to catalyst function. Further, this model can be used to predict the performance of each catalyst candidate in the greater database of virtual catalyst candidates. In this way, it is possible estimate the performance of tens of thousands of catalysts by experimentally testing a smaller subset. Using error assessment metrics, it is possible to understand the confidence in new predictions. An experimentalist using this tool can balance the predicted results (reward) with the prediction confidence (risk) when deciding which catalysts to synthesize next in an optimization campaign. These catalysts are synthesized and tested experimentally. At this stage, either the optimization is a success or the predicted values were incorrect and further optimization is required. In the case of the latter, the information can be fed back into the statistical learning model to refine the model, and this iterative process can be used to determine the optimal catalyst. In this body of work, we not only establish this workflow but quantitatively establish how best to execute each step. Herein, we evaluate several 3D molecular representations to determine how best to represent molecules. Several selection protocols are examined to best decide which set of molecules can be used to represent the library of interest. In addition, the number of reactions needed to make accurate, statistical learning models is evaluated. Taken together these components establish a tool ready to progress from the development stage to the utility stage. As such, current research endeavors focus on applying these tools to optimize new reactions.

A Unified Strategy for the Asymmetric Synthesis of Highly Substituted 1,2-Amino Alcohols Leading to Highly Substituted Bisoxazoline Ligands.

A general procedure for the asymmetric synthesis of highly substituted 1,2-amino alcohols in high yield and diastereoselectivity is described that uses organometallic additions of a wide range of nucleophiles to tert-butylsulfinimines as the key step. The addition of organolithium reagents to these imines follows a modified Davis model. The diastereoselectivity for this reaction depends significantly on both the nucleophile and electrophile. These highly substituted 1,2-amino alcohols are used to synthesize stereochemically diverse and structurally novel, polysubstituted 2,2'-methylene(bisoxazoline) ligands in high yields.

Structural Contributions to Autocatalysis and Asymmetric Amplification in the Soai Reaction.

Diisopropylzinc alkylation of pyrimidine aldehydes-the Soai reaction, with its astonishing attribute of amplifying asymmetric autocatalysis-occupies a unique position in organic chemistry and stands as an eminent challenge for mechanistic elucidation. A new paradigm of "mixed catalyst-substrate" experiments with pyrimidine and pyridine systems allows a disconnection of catalysis from autocatalysis, providing insights into the role played by reactant and alkoxide structure. The alkynyl substituent favorably tunes catalyst solubility, aggregation, and conformation while modulating substrate reactivity and selectivity. The alkyl groups and the heteroaromatic core play further complementary roles in catalyst aggregation and substrate binding. In the study of these structure-activity relationships, novel pyridine substrates demonstrating amplifying autocatalysis were identified. Comparison of three autocatalytic systems representing a continuum of nitrogen Lewis basicity strength suggests how the strength of N-Zn binding events is a predominant contributor toward the rate of autocatalytic progression.

Cautionary Guidelines for Machine Learning Studies with Combinatorial Datasets.

Regression modeling is becoming increasingly prevalent in organic chemistry as a tool for reaction outcome prediction and mechanistic interrogation. Frequently, to acquire the requisite amount of data for such studies, researchers employ combinatorial datasets to maximize the number of data points while limiting the number of discrete chemical entities required. An often-overlooked problem in modeling studies using combinatorial datasets is the tendency to fit on patterns in the datasets (i.e., the presence or absence of a reactant or catalyst) rather than to identify meaningful trends between descriptors and the response variable. Consequently, the generality and interpretability of such models suffer. This report illustrates these well-known pitfalls in a case study, demonstrates the necessary control experiments to identify when this property will be problematic, and suggests how to perform further validation to assess general applicability and interpretability of models trained using combinatorial datasets.

Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis.

Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.

Catalytic, Enantioselective Sulfenofunctionalization of Alkenes: Development and Recent Advances.

The last decade has witnessed a burgeoning of new methods for the enantioselective vicinal difunctionalization of alkenes initiated by electrophilic sulfenyl group transfer. The addition of sulfenium ions to alkenes results in the generation of chiral, non-racemic thiiranium ions. These highly reactive intermediates are susceptible to attack by a myriad of nucleophiles in a stereospecific ring-opening event to afford anti 1,2-sulfenofunctionalized products. The practical application of sulfenium ion transfer has been enabled by advances in the field of Lewis base catalysis. This Review will chronicle the initial discovery and characterization of thiiranium ion intermediates followed by the determination of their configurational stability and the challenges of developing enantioselective variants. Once the framework for the reactivity and stability of thiiranium ions has been established, a critical analysis of pioneering studies will be presented. Finally, a comprehensive discussion of modern synthetic applications will be categorized around the type of nucleophile employed for sulfenofunctionalization.

Demystifying the asymmetry-amplifying, autocatalytic behaviour of the Soai reaction through structural, mechanistic and computational studies.

The Soai reaction has profoundly impacted chemists' perspective of autocatalysis, chiral symmetry breaking, absolute asymmetric synthesis and its role in the origin of biological homochirality. Here we describe the unprecedented observation of asymmetry-amplifying autocatalysis in the alkylation of 5-(trimethylsilylethynyl)pyridine-3-carbaldehyde using diisopropylzinc. Kinetic studies with a surrogate substrate and spectroscopic analysis of a series of zinc alkoxides that incorporate specific structural mutations reveal a 'pyridine-assisted cube escape'. The new tetrameric cluster functions as a catalyst that activates the substrate through a two-point binding mode and poises a coordinated diisopropylzinc moiety for alkyl group transfer. Transition-state models leading to both the homochiral and heterochiral products were validated by density functional theory calculations. Moreover, experimental and computational analysis of the heterochiral complex provides a definitive explanation for the nonlinear behaviour of this system. Our deconstruction of the Soai system reveals the structural logic for autocatalyst evolution, function and substrate compatibility-a central mechanistic aspect of this iconic transformation.