Cobalt-electrocatalytic HAT for functionalization of unsaturated C-C bonds.

The study and application of transition metal hydrides (TMHs) has been an active area of chemical research since the early 1960s1, for energy storage, through the reduction of protons to generate hydrogen2,3, and for organic synthesis, for the functionalization of unsaturated C-C, C-O and C-N bonds4,5. In the former instance, electrochemical means for driving such reactivity has been common place since the 1950s6 but the use of stoichiometric exogenous organic- and metal-based reductants to harness the power of TMHs in synthetic chemistry remains the norm. In particular, cobalt-based TMHs have found widespread use for the derivatization of olefins and alkynes in complex molecule construction, often by a net hydrogen atom transfer (HAT)7. Here we show how an electrocatalytic approach inspired by decades of energy storage research can be made use of in the context of modern organic synthesis. This strategy not only offers benefits in terms of sustainability and efficiency but also enables enhanced chemoselectivity and distinct, tunable reactivity. Ten different reaction manifolds across dozens of substrates are exemplified, along with detailed mechanistic insights into this scalable electrochemical entry into Co-H generation that takes place through a low-valent intermediate.

Holistic prediction of enantioselectivity in asymmetric catalysis.

When faced with unfamiliar reaction space, synthetic chemists typically apply the reported conditions (reagents, catalyst, solvent and additives) of a successful reaction to a desired, closely related reaction using a new substrate type. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Consequently, an important goal in synthetic chemistry is the ability to transfer chemical observations quantitatively from one reaction to another. Here we present a holistic, data-driven workflow for deriving statistical models of one set of reactions that can be used to predict out-of-sample reactions. As a validating case study, we combined published enantioselectivity datasets that employ 1,1'-bi-2-naphthol (BINOL)-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines and developed statistical models. These models reveal the general interactions that impart asymmetric induction and allow the quantitative transfer of this information to new reaction components. This technique creates opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.

Predicting relative efficiency of amide bond formation using multivariate linear regression.

Amides are ubiquitous in biologically active natural products and commercial drugs. The most common strategy for introducing this functional group is the coupling of a carboxylic acid with an amine, which requires the use of a coupling reagent to facilitate elimination of water. However, the optimal reaction conditions often appear rather arbitrary to the specific reaction. Herein, we report the development of statistical models correlating measured rates to physical organic descriptors to enable the prediction of reaction rates for untested carboxylic acid/amine pairs. The key to the success of this endeavor was the development of an end-to-end data science­based workflow to select a set of coupling partners that are appropriately distributed in chemical space to facilitate statistical model development. By using a parameterization, dimensionality reduction, and clustering protocol, a training set was identified. Reaction rates for a range of carboxylic acid and primary alkyl amine couplings utilizing carbonyldiimidazole (CDI) as the coupling reagent were measured. The collected rates span five orders of magnitude, confirming that the designed training set encompasses a wide range of chemical space necessary for effective model development. Regressing these rates with high-level density functional theory (DFT) descriptors allowed for identification of a statistical model wherein the molecular features of the carboxylic acid are primarily responsible for the observed rates. Finally, out-of-sample amide couplings are used to determine the limitations and effectiveness of the model.

Development of Modular Nitrenium Bipolar Electrolytes for Possible Applications in Symmetric Redox Flow Batteries.

Amid the escalating integration of renewable energy sources, the demand for grid energy storage solutions, including non-aqueous organic redox flow batteries (oRFBs), has become ever more pronounced. oRFBs face a primary challenge of irreversible capacity loss attributed to the crossover of redox-active materials between half-cells. A possible solution for the crossover challenge involves utilization of bipolar electrolytes that act as both the catholyte and anolyte. Identifying such molecules poses several challenges as it requires a delicate balance between the stability of both oxidation states and energy density, which is influenced by the separation between the two redox events. We report the development of a diaminotriazolium redox-active core capable of producing two electronically distinct persistent radical species with typically extreme reduction potentials (E1/2red < -2 V, E1/2ox > +1 V, vs Fc0/+) and up to 3.55 V separation between the two redox events. Structure-property optimization studies allowed us to identify factors responsible for fine-tuning of potentials for both redox events, as well as separation between them. Mechanistic studies revealed two primary decomposition pathways for the neutral radical charged species and one for the radical biscation. Additionally, statistical modeling provided evidence for the molecular descriptors to allow identification of the structural features responsible for stability of radical species and to propose more stable analogues.

Development of a Deactivation-Resistant Dialkylbiarylphosphine Ligand for Pd-Catalyzed Arylation of Secondary Amines.

Despite the prevalence of N-heteroarenes in small-molecule pharmaceuticals, Pd-catalyzed C-N cross-coupling reactions of aryl halides and amines containing these rings remain challenging due to their ability to displace the supporting ligand via coordination to the metal center. To address this limitation, we report the development of a highly robust Pd catalyst supported by a new dialkylbiarylphosphine ligand, FPhos. The FPhos-supported catalyst effectively resists N-heteroarene-mediated catalyst deactivation to readily promote C-N coupling between a wide variety of Lewis-basic aryl halides and secondary amines, including densely functionalized pharmaceuticals. Mechanistic and structural investigations, as well as principal component analysis and density functional theory, elucidated two key design features that enable FPhos to overcome the limitations of previous ligands. First, the ligated Pd complex is stabilized through its conformational preference for the O-bound isomer, which likely resists coordination by N-heteroarenes. Second, 3',5'-disubstitution on the non-phosphorus-containing ring of FPhos creates the ideal steric environment around the Pd center, which facilitates binding by larger secondary amines while mitigating the formation of off-cycle palladacycle species.

Using Data Science Tools to Reveal and Understand Subtle Relationships of Inhibitor Structure in Frontal Ring-Opening Metathesis Polymerization.

The rate of frontal ring-opening metathesis polymerization (FROMP) using the Grubbs generation II catalyst is impacted by both the concentration and choice of monomers and inhibitors, usually organophosphorus derivatives. Herein we report a data-science-driven workflow to evaluate how these factors impact both the rate of FROMP and how long the formulation of the mixture is stable (pot life). Using this workflow, we built a classification model using a single-node decision tree to determine how a simple phosphine structural descriptor (Vbur-near) can bin long versus short pot life. Additionally, we applied a nonlinear kernel ridge regression model to predict how the inhibitor and selection/concentration of comonomers impact the FROMP rate. The analysis provides selection criteria for material network structures that span from highly cross-linked thermosets to non-cross-linked thermoplastics as well as degradable and nondegradable materials.

Electrocatalytic Asymmetric Nozaki-Hiyama-Kishi Decarboxylative Coupling: Scope, Applications, and Mechanism.

The first general enantioselective alkyl-Nozaki-Hiyama-Kishi (NHK) coupling reactions are disclosed herein by employing a Cr-electrocatalytic decarboxylative approach. Using easily accessible aliphatic carboxylic acids (via redox-active esters) as alkyl nucleophile synthons, in combination with aldehydes and enabling additives, chiral secondary alcohols are produced in a good yield with high enantioselectivity under mild reductive electrolysis. This reaction, which cannot be mimicked using stoichiometric metal or organic reductants, tolerates a broad range of functional groups and is successfully applied to dramatically simplify the synthesis of multiple medicinally relevant structures and natural products. Mechanistic studies revealed that this asymmetric alkyl e-NHK reaction was enabled by using catalytic tetrakis(dimethylamino)ethylene, which acts as a key reductive mediator to mediate the electroreduction of the CrIII/chiral ligand complex.

Computational Methods Enable the Prediction of Improved Catalysts for Nickel-Catalyzed Cross-Electrophile Coupling.

Cross-electrophile coupling has emerged as an attractive and efficient method for the synthesis of C(sp2)-C(sp3) bonds. These reactions are most often catalyzed by nickel complexes of nitrogenous ligands, especially 2,2'-bipyridines. Precise prediction, selection, and design of optimal ligands remains challenging, despite significant increases in reaction scope and mechanistic understanding. Molecular parameterization and statistical modeling provide a path to the development of improved bipyridine ligands that will enhance the selectivity of existing reactions and broaden the scope of electrophiles that can be coupled. Herein, we describe the generation of a computational ligand library, correlation of observed reaction outcomes with features of the ligands, and the in silico design of improved bipyridine ligands for Ni-catalyzed cross-electrophile coupling. The new nitrogen-substituted ligands display a 5-fold increase in selectivity for product formation versus homodimerization when compared to the current state of the art. This increase in selectivity and yield was general for several cross-electrophile couplings, including the challenging coupling of an aryl chloride with an N-alkylpyridinium salt.

14N to 15N Isotopic Exchange of Nitrogen Heteroaromatics through Skeletal Editing.

The selective modification of nitrogen heteroaromatics enables the development of new chemical tools and accelerates drug discovery. While methods that focus on expanding or contracting the skeletal structures of heteroaromatics are emerging, methods for the direct exchange of single core atoms remain limited. Here, we present a method for 14N → 15N isotopic exchange for several aromatic nitrogen heterocycles. This nitrogen isotope transmutation occurs through activation of the heteroaromatic substrate by triflylation of a nitrogen atom, followed by a ring-opening/ring-closure sequence mediated by 15N-aspartate to effect the isotopic exchange of the nitrogen atom. Key to the success of this transformation is the formation of an isolable 15N-succinyl intermediate, which undergoes elimination to give the isotopically labeled heterocycle. These transformations occur under mild conditions in high chemical and isotopic yields.

Enantioselective Sulfonimidamide Acylation via a Cinchona Alkaloid-Catalyzed Desymmetrization: Scope, Data Science, and Mechanistic Investigation.

Methods to access chiral sulfur(VI) pharmacophores are of interest in medicinal and synthetic chemistry. We report the desymmetrization of unprotected sulfonimidamides via asymmetric acylation with a cinchona-phosphinate catalyst. The desired products are formed in excellent yield and enantioselectivity with no observed bis-acylation. A data-science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling in order to inform mechanistic studies. Reaction kinetics, catalyst structural studies, and density functional theory (DFT) transition state analysis elucidated the turnover-limiting step to be the collapse of the tetrahedral intermediate and provided key insights into the catalyst-substrate structure-activity relationships responsible for the origin of the enantioselectivity. This study offers a reliable method for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores and serves as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques.

Interrogating the Mechanistic Features of Ni(I)-Mediated Aryl Iodide Oxidative Addition Using Electroanalytical and Statistical Modeling Techniques.

While the oxidative addition of Ni(I) to aryl iodides has been commonly proposed in catalytic methods, an in-depth mechanistic understanding of this fundamental process is still lacking. Herein, we describe a detailed mechanistic study of the oxidative addition process using electroanalytical and statistical modeling techniques. Electroanalytical techniques allowed rapid measurement of the oxidative addition rates for a diverse set of aryl iodide substrates and four classes of catalytically relevant complexes (Ni(MeBPy), Ni(MePhen), Ni(Terpy), and Ni(BPP)). With >200 experimental rate measurements, we were able to identify essential electronic and steric factors impacting the rate of oxidative addition through multivariate linear regression models. This has led to a classification of oxidative addition mechanisms, either through a three-center concerted or halogen-atom abstraction pathway based on the ligand type. A global heat map of predicted oxidative addition rates was created and shown applicable to a better understanding of the reaction outcome in a case study of a Ni-catalyzed coupling reaction.

Catalyst-Controlled Enantioselective and Regiodivergent Addition of Aryl Boron Nucleophiles to N-Alkyl Nicotinate Salts.

Dihydropyridines are versatile building blocks for the synthesis of pyridines, tetrahydropyridines, and piperidines. Addition of nucleophiles to activated pyridinium salts allows synthesis of 1,2-, 1,4-, or 1,6-dihydropyridines; however, this process often leads to a mixture of constitutional isomers. Catalyst-controlled regioselective addition of nucleophiles to pyridiniums has the potential to solve this problem. Herein, we report that the regioselective addition of boron-based nucleophiles to pyridinium salts can be accomplished by the choice of a Rh catalyst.

Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction.

The study of non-natural biocatalytic transformations relies heavily on empirical methods, such as directed evolution, for identifying improved variants. Although exceptionally effective, this approach provides limited insight into the molecular mechanisms behind the transformations and necessitates multiple protein engineering campaigns for new reactants. To address this limitation, we disclose a strategy to explore the biocatalytic reaction space and garner insight into the molecular mechanisms driving enzymatic transformations. Specifically, we explored the selectivity of an "ene"-reductase, GluER-T36A, to create a data-driven toolset that explores reaction space and rationalizes the observed and predicted selectivities of substrate/mutant combinations. The resultant statistical models related structural features of the enzyme and substrate to selectivity and were used to effectively predict selectivity in reactions with out-of-sample substrates and mutants. Our approach provided a deeper understanding of enantioinduction by GluER-T36A and holds the potential to enhance the virtual screening of enzyme mutants.

An Asymmetric Aromatic Finkelstein Reaction: A Platform for Remote Diarylmethane Desymmetrization.

A first-of-its-kind enantioselective aromatic Finkelstein reaction is disclosed for the remote desymmetrization of diarylmethanes. The reaction operates through a copper-catalyzed C-I bond-forming event, and high levels of enantioselectivity are achieved through the deployment of a tailored guanidinylated peptide ligand. Strategic use of transition-metal-mediated reactions enables the chemoselective modification of the aryl iodide products; thus, the synthesis of a diverse set of otherwise difficult-to-access diarylmethanes with excellent levels of selectivity is realized from a common intermediate. A mixed experimental/computational analysis of steric parameters and substrate conformations identifies the importance of remote conformational effects as a key to achieving high enantioselectivity in this desymmetrization reaction.

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands.

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.

Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides.

New methods for the general asymmetric synthesis of sulfonimidamides are of great interest due to their applications in medicinal chemistry, agrochemical discovery, and academic research. We report a palladium-catalyzed cross-coupling method for the enantioselective aryl-carbonylation of sulfonimidamides. Using data science techniques, a virtual library of calculated bisphosphine ligand descriptors was used to guide reaction optimization by effectively sampling the catalyst chemical space. The optimized conditions identified using this approach provided the desired product in excellent yield and enantioselectivity. As the next step, a data science-driven strategy was also used to explore a diverse set of aryl and heteroaryl iodides, providing key information about the scope and limitations of the method. Furthermore, we tested a range of racemic sulfonimidamides for compatibility of this coupling partner. The developed method offers a general and efficient strategy for accessing enantioenriched sulfonimidamides, which should facilitate their application in industrial and academic settings.

Exploiting non-covalent π interactions for catalyst design.

Molecular recognition, binding and catalysis are often mediated by non-covalent interactions involving aromatic functional groups. Although the relative complexity of these so-called π interactions has made them challenging to study, theory and modelling have now reached the stage at which we can explain their physical origins and obtain reliable insight into their effects on molecular binding and chemical transformations. This offers opportunities for the rational manipulation of these complex non-covalent interactions and their direct incorporation into the design of small-molecule catalysts and enzymes.

A Physical Organic Approach towards Statistical Modeling of Tetrazole and Azide Decomposition.

Nitrogen atom-rich heterocycles and organic azides have found extensive use in many sectors of modern chemistry from drug discovery to energetic materials. The prediction and understanding of their energetic properties are thus key to the safe and effective application of these compounds. In this work, we disclose the use of multivariate linear regression modeling for the prediction of the decomposition temperature and impact sensitivity of structurally diverse tetrazoles and organic azides. We report a data-driven approach for property prediction featuring a collection of quantum mechanical parameters and computational workflows. The statistical models reported herein carry predictive accuracy as well as chemical interpretability. Model validation was successfully accomplished via tetrazole test sets with parameters generated exclusively in silico. Mechanistic analysis of the statistical models indicated distinct divergent pathways of thermal and impact-initiated decomposition.

Mechanistically Guided Workflow for Relating Complex Reactive Site Topologies to Catalyst Performance in C-H Functionalization Reactions.

Leveraging congested catalyst scaffolds has emerged as a key strategy for altering innate substrate site-selectivity profiles in C-H functionalization reactions. Similar to enzyme active sites, optimal small molecule catalysts often feature reactive cavities tailored for controlling substrate approach trajectories. However, relating three-dimensional catalyst shape to reaction output remains a formidable challenge, in part due to the lack of molecular features capable of succinctly describing complex reactive site topologies in terms of numerical inputs for machine learning applications. Herein, we present a new set of descriptors, "Spatial Molding for Approachable Rigid Targets" (SMART), which we have applied to quantify reactive site spatial constraints for an expansive library of dirhodium catalysts and to predict site-selectivity for C-H functionalization of 1-bromo-4-pentylbenzene via donor/acceptor carbene intermediates. Optimal site-selectivity for the terminal methylene position was obtained with Rh2(S-2-Cl-5-MesTPCP)4 (30.9:1 rr, 14:1 dr, 87% ee), while C-H functionalization at the electronically activated benzylic site was increasingly favored for Rh2(TPCP)4 catalysts lacking an ortho-Cl, Rh2(S-PTAD)4, and Rh2(S-TCPTAD)4, respectively. Intuitive global site-selectivity models for 25 disparate dirhodium catalysts were developed via multivariate linear regression to explicitly assess the contributing roles of steric congestion and dirhodium-carbene electrophilicity in controlling the site of C-H functionalization. The workflow utilizes spatial classification to extract descriptors only for reactive catalyst conformers, a nuance that may be widely applicable for establishing close correspondence between ground-state model systems and transition states. Broader still, SMART descriptors are amenable for delineating salient reactive site features to predict reactivity in other chemical and biological contexts.

Leveraging Regio- and Stereoselective C(sp3)-H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias.

The C-H functionalization of silyl ethers via carbene-induced C-H insertion represents an efficient synthetic disconnection strategy. In this work, site- and stereoselective C(sp3)-H functionalization at α, γ, δ, and even more distal positions to the siloxy group has been achieved using donor/acceptor carbene intermediates. By exploiting the predilections of Rh2(R-TCPTAD)4 and Rh2(S-2-Cl-5-BrTPCP)4 catalysts to target either more electronically activated or more spatially accessible C-H sites, respectively, divergent desired products can be formed with good diastereocontrol and enantiocontrol. Notably, the reaction can also be extended to enable desymmetrization of meso silyl ethers. Leveraging the broad substrate scope examined in this study, we have trained a machine learning classification model using logistic regression to predict the major C-H functionalization site based on intrinsic substrate reactivity and catalyst propensity for overriding it. This model enables prediction of the major product when applying these C-H functionalization methods to a new substrate of interest. Applying this model broadly, we have demonstrated its utility for guiding late-stage functionalization in complex settings and developed an intuitive visualization tool to assist synthetic chemists in such endeavors.