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Catalytic enantioselective methods that are generally applicable to a broad range of substrates are rare. We report a strategy for the oxidative desymmetrization of meso -diols predicated on a nontraditional catalyst optimization protocol by using a panel of screening substrates rather than a singular model substrate. Critical to this approach was rational modulation of a peptide sequence in the catalyst incorporating a distinct aminoxyl-based active residue. A general catalyst emerged, providing high selectivity in the delivery of enantioenriched lactones across a broad range of diols, while also achieving up to ~100,000 turnovers.

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.

Hydrogen bond-based organocatalysts rely on networks of attractive noncovalent interactions (NCIs) to impart enantioselectivity. As a specific example, aryl pyrrolidine substituted urea, thiourea, and squaramide organocatalysts function cooperatively through hydrogen bonding and difficult-to-predict NCIs as a function of the reaction partners. To uncover the synergistic effect of the structural components of this catalyst class, we applied data science tools to study various model reactions using a derivatized, aryl pyrrolidine-based, hydrogen-bond donor (HBD) catalyst library. Through a combination of experimentally collected data and data mined from previous reports, statistical models were constructed, illuminating the general features necessary for high enantioselectivity. A distinct dependence on the identity of the electrophilic reaction partner and HBD catalyst is observed, suggesting that a general interaction is conserved throughout the reactions analyzed. The resulting models also demonstrate predictive capability by the successful improvement of a previously reported reaction using out-of-sample reaction components. Overall, this study highlights the power of data science in exploring mechanistic hypotheses in asymmetric HBD catalysis and provides a prediction platform applicable in future reaction optimization.

Prenyltransfer is an early-stage carbon-hydrogen bond (C-H) functionalization prevalent in the biosynthesis of a diverse array of biologically active bacterial, fungal, plant, and metazoan diketopiperazine (DKP) alkaloids. Toward the development of a unified strategy for biocatalytic construction of prenylated DKP indole alkaloids, we sought to identify and characterize a substrate-permissive C2 reverse prenyltransferase (PT). As the first tailoring event within the biosynthesis of cytotoxic notoamide metabolites, PT NotF catalyzes C2 reverse prenyltransfer of brevianamide F. Solving a crystal structure of NotF (in complex with native substrate and prenyl donor mimic dimethylallyl S-thiolodiphosphate (DMSPP)) revealed a large, solvent-exposed active site, intimating NotF may possess a significantly broad substrate scope. To assess the substrate selectivity of NotF, we synthesized a panel of 30 sterically and electronically differentiated tryptophanyl DKPs, the majority of which were selectively prenylated by NotF in synthetically useful conversions (2 to >99%). Quantitative representation of this substrate library and development of a descriptive statistical model provided insight into the molecular origins of NotF's substrate promiscuity. This approach enabled the identification of key substrate descriptors (electrophilicity, size, and flexibility) that govern the rate of NotF-catalyzed prenyltransfer, and the development of an "induced fit docking (IFD)-guided" engineering strategy for improved turnover of our largest substrates. We further demonstrated the utility of NotF in tandem with oxidative cyclization using flavin monooxygenase, BvnB. This one-pot, in vitro biocatalytic cascade enabled the first chemoenzymatic synthesis of the marine fungal natural product, (-)-eurotiumin A, in three steps and 60% overall yield.

Noncovalent interactions (NCIs) are critical elements of molecular recognition in a wide variety of chemical contexts. While NCIs have been studied extensively for closed-shell molecules and ions, very little is understood about the structures and properties of NCIs involving free radical intermediates. In this report, we describe a detailed mechanistic study of the enantioselective radical hydroamination of alkenes with sulfonamides and present evidence suggesting that the basis for asymmetric induction in this process arises from attractive NCIs between a neutral sulfonamidyl radical intermediate and a chiral phosphoric acid (CPA). We describe experimental, computational, and data science-based evidence that identifies the specific radical NCIs that form the basis for the enantioselectivity. Kinetic studies support that C-N bond formation determines the enantioselectivity. Density functional theory investigations revealed the importance of both strong H-bonding between the CPA and the

The study and application of transition metal hydrides (TMH), an active area of chemical research since the early 1960’s(1), has been broadly bifurcated into fields focused on energy storage through the reduction of protons to generate hydrogen(2,3) and in organic synthesis for the functionalization of unsaturated C–C, C–O, and C–N bonds.(4,5) In the former instance, electrochemical means for driving such reactivity has been commonplace since the 1950’s(6) 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, Co-based TMHs have found widespread use for the derivatization of olefins and alkynes in complex molecule construction, often via a net hydrogen atom transfer (HAT).(7) Here, we show how an electrocatalytic approach inspired by decades of energy storage precedent can be leveraged 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 unique and tunable reactivity. Ten different reaction manifolds across dozens of substrates are thus exemplified, along with detailed mechanistic insight on this scalable electrochemical entry to Co-H generation that uniquely takes place through a low-valent intermediate.

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 Rh

Biomolecular condensates regulate cellular function by compartmentalizing molecules without a surrounding membrane1,2. Condensate functions are believed to arise from the specific exclusion or enrichment of molecules3-5. Thus, understanding the principles governing condensate composition is critical to characterizing condensate function. The molecular bases of macromolecular composition have been studied in detail for several condensates6-9, but partitioning of small molecules into condensates remains poorly understood. Using mass spectrometry with validation by fluorescence microscopy, we quantified partitioning of ∼1700 metabolites and FDA-approved drugs into four condensates composed of different DNA and/or protein scaffolds. We found that partitioning spanned from ∼100-fold exclusion to ∼10,000-fold enrichment. Strong correlations between the different condensates suggest an underlying physical similarity despite disparate macromolecular components. Strongly partitioning molecules generally did not bind condensate-forming proteins with high affinity under conditions where condensates do not form, suggesting only a minor role for stereospecific interactions in partitioning. We developed a machine learning model that accurately predicts partitioning using only physicochemical properties of the compounds. The strongest predictors of partitioning were features related to aqueous solubility and hydrophobicity. Small molecule partitioning was similar for condensates in aqueous buffer and in concentrated cell extracts, suggesting analogous behaviors in vivo. Together, the data and model suggest that small molecule partitioning is not generally based on high-affinity, stereospecific interactions with scaffolds, but rather on physical properties of the compounds, and their compatibility with an emergent hydrophobic environment within the condensate. Our results will aid design of chemical compounds that target biomolecular condensates and reveal unexpected physical and functional similarities of distinct condensates.

From the preparation of pharmaceuticals to enzymatic construction of natural products, carbocations are central to molecular synthesis. Although these reactive intermediates are engaged in stereoselective processes in nature, exerting enantiocontrol over carbocations with synthetic catalysts remains challenging. Many resonance-stabilized tricoordinated carbocations, such as iminium and oxocarbenium ions, have been applied in catalytic enantioselective reactions. However, their dicoordinated counterparts (aryl and vinyl carbocations) have not, despite their emerging utility in chemical synthesis. We report the discovery of a highly enantioselective vinyl carbocation carbon–hydrogen (C–H) insertion reaction enabled by imidodiphosphorimidate organocatalysts. Active site confinement featured in this catalyst class not only enables effective enantiocontrol but also expands the scope of vinyl cation C–H insertion chemistry, which broadens the utility of this transition metal–free C(sp 3 )–H functionalization platform.

An efficient asymmetric synthesis of a potent KRAS G12C covalent inhibitor, GDC-6036 (

Organic chemistry is replete with complex relationships: for example, how a reactant’s structure relates to the resulting product formed; how reaction conditions relate to yield; how a catalyst’s structure relates to enantioselectivity. Questions like these are at the foundation of understanding reactivity and developing novel and improved reactions. An approach to probing these questions that is both longstanding and contemporary is data-driven modeling. Here, we provide a synopsis of the history of data-driven modeling in organic chemistry and the terms used to describe these endeavors. We include a timeline of the steps that led to its current state. The case studies included highlight how, as a community, we have advanced physical organic chemistry tools with the aid of computers and data to augment the intuition of expert chemists and to facilitate the prediction of structure–activity and structure–property relationships., Data science has emerged as a powerful tool to study organic chemistry. Herein, we provide a history of data-driven modeling in organic chemistry and a discussion of the current state of the field.

Autonomous process optimization involves the human intervention-free exploration of a range of pre-defined process parameters in order to improve responses such as reaction yield and product selectivity. Utilizing off-the-shelf components, we developed a closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times. Upon implementation of our system in the autonomous optimization of a palladium-catalyzed stereoselective Suzuki-Miyaura coupling, we found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization. In addition, we found that categorical parameters such as phosphine ligand were vital to determining the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing an element of bias into the experimental design. In seeking a systematic method for the selection of a diverse set of phosphine ligands fully representative of the chemical space, we developed a strategy that leveraged computed molecular descriptor clustering analysis. This strategy allowed for the successful autonomous optimization of a stereoselective Suzuki-Miyaura coupling between a vinyl sulfonate and an arylboronic acid to selectively generate the E-product isomer in high yield.

Cyanopyridines and cyanophenylpyridines were investigated as anolytes for nonaqueous redox flow batteries (RFBs). The three isomers of cyanopyridine are reduced at potentials of -2.2 V or lower vs. ferrocene

The catalysis by a π-allyl-Co/Ni complex has drawn significant attention recently due to its distinct reactivity in reductive Co/Ni-catalyzed allylation reactions. Despite significant success in reaction development, the critical oxidative addition mechanism to form the π-allyl-Co/Ni complex remains unclear. Herein, we present a study to investigate this process with four catalysis-relevant complexes: Co(

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(sp

Herein, we report a reaction that selectively generates 3-arylpyridine and quinoline motifs by inserting aryl carbynyl cation equivalents into pyrrole and indole cores, respectively. By employing α-chlorodiazirines as thermal precursors to the corresponding chlorocarbenes, the traditional haloform-based protocol central to the parent Ciamician-Dennstedt rearrangement can be modified to directly afford 3-(hetero)arylpyridines and quinolines. Chlorodiazirines are conveniently prepared in a single step by oxidation of commercially available amidinium salts. Selectivity as a function of pyrrole substitution pattern was examined, and a predictive model based on steric effects is put forward, with DFT calculations supporting a selectivity-determining cyclopropanation step. Computations surprisingly indicate that the stereochemistry of cyclopropanation is of little consequence to the subsequent electrocyclic ring opening that forges the pyridine core, due to a compensatory homoaromatic stabilization that counterbalances orbital-controlled torquoselectivity effects. The utility of this skeletal transform is further demonstrated through the preparation of quinolinophanes and the skeletal editing of pharmaceutically relevant pyrroles.

The site-specific oxidation of strong C(sp(3))–H bonds is of uncontested utility in organic synthesis. From simplifying access to metabolites and late-stage diversification of lead compounds to truncating retrosynthetic plans, there is a growing need for new reagents and methods for achieving such a transformation in both academic and industrial circles. One main drawback of current chemical reagents is the lack of diversity with regard to structure and reactivity that prevents a combinatorial approach for rapid screening to be employed. In that regard, directed evolution still holds the greatest promise for achieving complex C–H oxidations in a variety of complex settings. Herein we present a rationally designed platform that provides a step toward this challenge using N-ammonium ylides as electrochemically driven oxidants for site-specific, chemoselective C(sp(3))–H oxidation. By taking a first-principles approach guided by computation, these new mediators were identified and rapidly expanded into a library using ubiquitous building blocks and trivial synthesis techniques. The ylide-based approach to C–H oxidation exhibits tunable selectivity that is often exclusive to this class of oxidants and can be applied to real-world problems in the agricultural and pharmaceutical sectors.

The redox-relay Heck reaction is a powerful method for the construction of enantioenriched quaternary stereocenters remote from existing functional groups. However, there has been little success in the design of site-selective alkene functionalization based on these methods. Herein, we show that experimentally determined rates can be used to train a multivariate linear regression model capable of predicting the rate of a specific relay Heck reaction, allowing for the site-selective functionalization of diene substrates.

Redox catalysis has been broadly utilized in electrochemical synthesis due to its kinetic advantages over direct electrolysis. The appropriate choice of redox mediator can avoid electrode passivation and overpotential, which strongly inhibit the efficient activation of substrates in electrolysis. Despite the benefits brought by redox catalysis, establishing the precise nature of substrate activation remains challenging. Herein, we determine that a Co(i) complex bearing two N,N,N-tridentate ligands acts as a competent redox catalyst for the reduction of benzyl bromide substrates. Kinetic studies combining electroanalytical techniques with multivariable linear-regression analysis were conducted, disclosing an outer-sphere electron-transfer mechanism, which occurs in concert with C–Br bond cleavage. Furthermore, we apply a pattern recognition platform to distinguish between mechanisms in the activation of benzyl bromides, found to be dependent on the ligation state of the cobalt(i) center and ligand used., Through kinetic studies combining electroanalytical techniques with multivariable linear-regression (MLR) analysis, a pattern recognition platform is established to determine the electron-transfer mechanism (inner-sphere or outer-sphere) of an electrochemical reduction of benzyl bromides, mediated by different cobalt complexes.

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 science–based 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.

Ti(salen) complexes catalyze the asymmetric [3 + 2] cycloaddition of cyclopropyl ketones with alkenes. While high enantioselectivities are achieved with electron-rich alkenes, electron-deficient alkenes are less selective. Herein, we describe mechanistic studies to understand the origins of catalyst and substrate trends in an effort to identify a more general catalyst. Density functional theory (DFT) calculations of the selectivity determining transition state revealed the origin of stereochemical control to be catalyst distortion, which is largely influenced by the chiral backbone and adamantyl groups on the salicylaldehyde moieties. While substitution of the adamantyl groups was detrimental to the enantioselectivity, mechanistic information guided the development of a set of eight new Ti(salen) catalysts with modified diamine backbones. These catalysts were evaluated with four electron-deficient alkenes to develop a three-parameter statistical model relating enantioselectivity to physical organic parameters. This statistical model is capable of quantitative prediction of enantioselectivity with structurally diverse alkenes. These mechanistic insights assisted the discovery of a new Ti(salen) catalyst, which substantially expanded the reaction scope and significantly improved the enantioselectivity of synthetically interesting building blocks.

We report an iodoarene-catalyzed enantioselective synthesis of β,β-difluoroalkyl halide building blocks. The transformation involves an oxidative rearrangement of α-bromostyrenes, utilizing HF–pyridine as the fluoride source and m-CPBA as the stoichiometric oxidant. A catalyst decomposition pathway was identified, which, in tandem with catalyst structure–activity relationship studies, facilitated the development of an improved catalyst providing higher enantioselectivity with lower catalyst loadings. The versatility of the difluoroalkyl bromide products was demonstrated via highly enantiospecific substitution reactions with suitably reactive nucleophiles. The origins of enantioselectivity were investigated using computed interaction energies of simplified catalyst and substrate structures, providing evidence for both CH–π and π–π transition state interactions as critical features.

By now, most of us in the field of organic chemistry have become aware of the recent Perspective Article by Hudlicky published on the Angewandte Chemie, International Edition website(1) and then quickly removed as a result of rapid and strong denunciation on social media and in other forums. We have had complex emotional responses to the opinions expressed in this piece regarding the effects of diversity and inclusion efforts in chemistry: anger, that such regressive views were provided a platform in one of our leading chemistry journals; surprise, that the piece made it through the peer review process; and disappointment, that these views continue to persist, despite our hope that the climate for researchers in organic chemistry had improved since we were all trainees.

The mode of asymmetric induction in an enantioselective intramolecular allylic substitution reaction catalyzed by a combination of palladium and a chiral phosphoric acid was investigated by a combined experimental and statistical modeling approach. Experiments to probe nonlinear effects, the reactivity of deuterium-labeled substrates, and control experiments revealed that nucleophilic attack to the π-allylpalladium intermediate is the enantio-determining step, in which the chiral phosphate anion is involved in stereoinduction. Using multivariable linear regression analysis, we determined that multiple noncovalent interactions with the chiral environment of the phosphate anion are integral to enantiocontrol in the transition state. The synthetic protocol to form chiral pyrrolidines was further applied to the asymmetric construction of C–O bonds at fully substituted carbon centers in the synthesis of chiral 2,2-disubstituted benzomorpholines.

The development of an electrochemically driven, ruthenium-catalyzed C-H hydroxylation reaction of amine-derived substrates bearing tertiary C-H bonds is described. The reaction is performed under constant current electrolysis in a divided cell to afford alcohol products in yields comparable to those of our previously reported process, which requires the use of stoichiometric H

Herein we report the first highly enantioselective allenoate-Claisen rearrangement using doubly axially chiral phosphate sodium salts as catalysts. This synthetic method provides access to β-amino acid derivatives with vicinal stereocenters in up to 95% ee. We also investigated the mechanism of enantioinduction by transition state (TS) computations with DFT as well as statistical modeling of the relationship between selectivity and the molecular features of both the catalyst and substrate. The mutual interactions of charge-separated regions in both the zwitterionic intermediate generated by reaction of an amine to the allenoate and the Na(+)-salt of the chiral phosphate leads to an orientation of the TS in the catalytic pocket that maximizes favorable noncovalent interactions. Crucial arene–arene interactions at the periphery of the catalyst lead to a differentiation of the TS diastereomers. These interactions were interrogated using DFT calculations and validated through statistical modeling of parameters describing noncovalent interactions.

Medicinal chemistry campaigns set the foundation for streamlined molecular design strategies through the development of quantitative structure-activity models. Our group's enduring underlying interest in reaction mechanism propelled our adaption of a similar strategy to unite mechanistic interrogation and catalyst optimization by relating reaction outputs to molecular descriptors. Through collaborative opportunities, we have recently expanded these predictive statistical modeling tools to electrocatalysis and the design of redox-active organic molecules for application as electrolytes in nonaqueous redox flow batteries. Utilizing small, strategically designed data sets for a given core structure, we develop predictive statistical models that enable rapid virtual screening campaigns to identify analogues with enhanced properties. This process relates structural parameters to the output of interest, providing insight into the structural features that influence the output under study. Furthermore, the weighting of the coefficients for each parameter in the model can furnish mechanistic insight. Such a synergistic implementation of experimental and computational tools for mechanistic insight provides a means of forecasting properties of analogues without necessitating the synthesis and analysis of each molecule of interest. Through collaborative efforts, we have demonstrated the effectiveness of these tools for predicting diverse outputs such as stability, redox potential, and nonaqueous solubility. In this Account, we outline our entry into the field of organic electrochemistry and the implementation of statistical modeling tools for designing organic electrolytes. Through these projects we were exposed to the power of electrochemical techniques as a mechanistic tool, which has provided access to critical information that would otherwise be difficult to obtain. Utilizing electroanalytical techniques, we have quantified the rates of disproportionation of a variety of cobalt complexes and developed statistical models that provide critical insight into understanding of fundamental processes involved in the disproportionation of organometallic complexes. Electroanalytical tools have also been effective in elucidating the active catalyst oxidation state in different catalytic organometallic systems for C-H functionalization. Thus, our foray into electrolyte design and electrocatalysis, in which the statistical modeling tools developed for mechanistic insight were applied in a new context, came full circle to the core foundation of our group: mechanistic understanding.

The direct, transition metal-catalyzed carboxylation of organohalides with carbon dioxide is a highly desirable transformation in organic synthesis as it utilizes feedstock chemicals and delivers carboxylic acids –among the most utilized class of organic molecules. Phenyl acetic acids, in particular, are privileged motifs that appear in many pharmaceuticals and biologically active compounds. This article reports the development of a sustainable and selective cobalt-catalyzed electrochemical carboxylation of benzyl halides with CO2 to generate phenyl acetic acids. The success of this transformation is enabled by the development of low-coordinate cobalt/pyrox complexes as electrocatalysts to convert various benzyl chlorides and bromides to their corre-sponding phenyl/heteroaryl acetic acids with high selectivity over undesired homocoupling of the benzyl halides. The combina-tion of electroanalytical methods, simulation studies, control reactions, and first-principles density functional theory (DFT) calculations informed the mechanistic analysis of this reaction. An EC’C-type activation mechanism of benzyl halides, which is unique to Co(II)/pyrox electrocatalysts, provides the rationalization of the exceptional observed selectivity for carboxylation. Specifically, the Co(II)/pyrox catalyst undergoes reduction to Co(I) followed by halogen abstraction and a favorable radical rebound to Co(II)/pyrox to form alkyl–Co(III) intermediates. Although voltammetry only shows a single electron transfer step, bulk electrolysis shows a two electron process and using DFT calculations, the intermediates are proposed to undergo two-electron reduction to alkyl–Co(I) followed by a ZnCl2-assisted CO2 insertion to form the carboxylated adducts with regenera-tion of Co(I)/pyrox.

We report the development of a method to diastereoselectively access tetrasubstituted alkenes via nickel-catalyzed Suzuki-Miyaura cross-couplings of enol tosylates and boronic acid esters. Either diastereomeric product was selectively accessed from a mixture of enol tosylate starting material diastereomers in a convergent reaction by judicious choice of the ligand and reaction conditions. A similar protocol also enabled a divergent synthesis of each product isomer from diastereomerically pure enol tosylates. Notably, high-throughput optimization of the monophosphine ligands was guided by chemical space analysis of the kraken library to ensure a diverse selection of ligands was examined. Stereoelectronic analysis of the results provided insight into the requirements for reactive and selective ligands in this transformation. The synthetic utility of the optimized catalytic system was then probed in the stereoselective synthesis of various tetrasubstituted alkenes, with yields up to 94% and diastereomeric ratios up to 99:1 Z/E and 93:7 E/Z observed. Moreover, a detailed computational analysis and experimental mechanistic studies provided key insights into the nature of the underlying isomerization process impacting selectivity in the cross-coupling.

Multivariate linear regression analysis (MLR) is used to unify and correlate different categories of asymmetric Cu-bisoxazoline (BOX) catalysis. The versatility of Cu-BOX complexes has been leveraged for several types of enantioselective transformations including cyclopropanation, Diels-Alder cycloadditions and difunctionalization of alkenes. Statistical tools and extensive molecular featurization has guided the development of an inclusive linear regression model, providing a predictive platform and readily interpretable descriptors. Mechanism-specific categorization of curated datasets and parameterization of reaction components allows for simultaneous analysis of disparate organometallic intermediates such as carbenes and Lewis acid adducts, all unified by a common ligand scaffold and metal ion. Additionally, this workflow permitted the development of a complementary linear regression model correlating analogous BOX-catalyzed reactions employing Ni, Fe, Mg, and Pd complexes. Comparison of ligand parameters in each model reveals the relevant structural requirements necessary for high selectivity. Overall, this strategy highlights the utility of MLR analysis in exploring mechanistically driven correlations across a diverse chemical space in organometallic chemistry and presents an applicable workflow for related ligand classes.

Which phosphines squeeze together? Phosphine ligands coordinated to palladium and nickel are essential tools for assembling the backbones of pharmaceutical compounds. For decades, descriptors that characterize spatial bulk have helped to guide phosphine optimization. However, these descriptors tend to apply to ideal geometries of a single ligand. Newman-Stonebraker et al . introduce a descriptor that considers how the ligand conformation might change in a crowded environment. Specifically, they found that the minimum percentage buried volume accurately predicts when one or two of a particular ligand will coordinate to a metal center, frequently a key determinant of successful catalysis. —JSY

Prenyltransfer is an early-stage carbon–hydrogen bond (C–H) functionalization prevalent in the biosynthesis of a diverse array of biologically active bacterial, fungal, plant, and metazoan diketopiperazine (DKP) alkaloids. Towards the development of a unified strategy for biocatalytic construction of prenylated DKP indole alkaloids, we sought to identify and characterize a substrate-permissive C2 reverse prenyltransferase (PT). In the biosynthesis of cytotoxic notoamide metabolites, PT NotF is responsible for catalyzing the first tailoring event of C2 reverse prenyltransfer of brevianamide F (cyclo(L-Trp-L-Pro)). Obtaining a high-resolution crystal structure of NotF (in complex with native substrate and prenyl donor mimic dimethylallyl S-thiolodiphosphate (DMSPP)) revealed a large, solvent exposed substrate binding site, intimating NotF may possess significant substrate promiscuity. To assess the full potential of NotF’s broad substrate selectivity, we synthesized a panel of 30 tryptophanyl DKPs with a suite of sterically and electronically differentiated amino acids, which were selectively prenylated by NotF in often synthetically useful conversions (2 to >99%). Quantitative representation of this substrate library enabled the development of a descriptive statistical model that provided insight into the origins of NotF’s substrate promiscuity. Through this unique approach for understanding enzyme scope, we identified key substrate descriptors such as electrophilicity, size, and flexibility, that govern enzymatic turnover by NotF. Additionally, we demonstrated the ability to couple NotF-catalyzed prenyltransfer with oxidative cyclization using recently characterized flavin monooxygenase, BvnB, from the brevianamide biosynthetic pathway. This one-pot, in vitro biocatalytic cascade proceeds with exceptional substrate recognition, and enabled the first chemoenzymatic synthesis of the marine fungal natural product, (–)-eurotiumin A, in three steps and 60% overall yield.

Prenyltransfer is an early-stage carbon–hydrogen bond (C–H) functionalization prevalent in the biosynthesis of a diverse array of biologically active bacterial, fungal, plant, and metazoan diketopiperazine (DKP) alkaloids. Towards the development of a unified strategy for biocatalytic construction of prenylated DKP indole alkaloids, we sought to identify and characterize a substrate-permissive C2 reverse prenyltransferase (PT). In the biosynthesis of cytotoxic notoamide metabolites, PT NotF is responsible for catalyzing the first tailoring event of C2 reverse prenyltransfer of brevianamide F (cyclo(L-Trp-L-Pro)). Obtaining a high-resolution crystal structure of NotF (in complex with native substrate and prenyl donor mimic dimethylallyl S-thiolodiphosphate (DMSPP)) revealed a large, solvent exposed substrate binding site, intimating NotF may possess significant substrate promiscuity. To assess the full potential of NotF’s broad substrate selectivity, we synthesized a panel of 30 tryptophanyl DKPs with a suite of sterically and electronically differentiated amino acids, which were selectively prenylated by NotF in often synthetically useful conversions (2 to >99%). Quantitative representation of this substrate library enabled the development of a descriptive statistical model that provided insight into the origins of NotF’s substrate promiscuity. Through this unique approach for understanding enzyme scope, we identified key substrate descriptors such as electrophilicity, size, and flexibility, that govern enzymatic turnover by NotF. Additionally, we demonstrated the ability to couple NotF-catalyzed prenyltransfer with oxidative cyclization using recently characterized flavin monooxygenase, BvnB, from the brevianamide biosynthetic pathway. This one-pot, in vitro biocatalytic cascade proceeds with exceptional substrate recognition, and enabled the first chemoenzymatic synthesis of the marine fungal natural product, (–)-eurotiumin A, in three steps and 60% overall yield.

This report describes the design of diaminocyclopropenium-phenothiazine hybrid catholytes for non-aqueous redox flow batteries. The molecules are synthesized in a rapid and modular fashion by appending a diaminocyclopropenium (DAC) substituent to the nitrogen of the phenothiazine. Combining a versatile C-N coupling protocol (which provides access to diverse derivatives) with computation and structure-property analysis enabled the identification of a catholyte that displays stable two-electron cycling at potentials of 0.64 and 1.00 V vs. Fc/Fc+ as well as high solubility in all oxidation states (≥0.45 M in TBAPF6 /MeCN). This catholyte was deployed in a high energy density two-electron RFB, exhibiting >90 % capacity retention over 266 hours of flow cell cycling at >0.5 M electron concentration.

The study and application of transition metal hydrides (TMH) has been an active area of chemical research since the early 1960’s. The use of TMHs has been broadly bifurcated into fields focused on energy storage through the reduction of protons to generate hydrogen and in organic synthesis for the functionalization of unsaturated C–C, C–O, and C–N bonds. In the former instance, electrochemical means for driving such reactivity has been commonplace since the 1950’s. In contrast, the use of stoichiometric exogenous organic and metal-based reductants to harness the power of TMHs in synthetic chemistry remains the norm. In particular, Co-based TMHs have found widespread use for the derivatization of olefins and alkynes in complex molecule construction, often via a net hydrogen atom transfer (HAT). Here, we show how an electrocatalytic approach inspired by decades of energy storage precedent can be leveraged in the context of modern organic synthesis. Such an approach not only offers benefits in terms of sustainability and efficiency but also enables enhanced chemoselectivity and unique and tunable reactivity. Ten different reaction manifolds across dozens of substrates are thus exemplified, along with a detailed mechanistic and computational analysis of this scalable electrochemical entry into Co-H chemistry.

High redox potential, two-electron organic catholytes for nonaqueous redox flow batteries were developed by appending diaminocyclopropenium (DAC) substituents to phenazine and phenothiazine cores. The parent heterocycles exhibit two partially reversible oxidations at moderate potentials [both at lower than 0.7 V vs ferrocene/ferrocenium (Fc/Fc+)]. The incorporation of DAC substituents has a dual effect on these systems. The DAC groups increase the redox potential of both couples by ∼300 mV while simultaneously rendering the second oxidation (which occurs at 1.20 V vs Fc/Fc+ in the phenothiazine derivative) reversible. The electron-withdrawing nature of the DAC unit is responsible for the increase in redox potential, while the DAC substituents stabilize oxidized forms of the molecules through resonance delocalization of charge and unpaired spin density. These new catholytes were deployed in two-electron redox flow batteries that exhibit voltages of up to 2.0 V and no detectable crossover over 250 cycles.

In reaction discovery, the search space of discrete reaction parameters such as catalyst structure is often not explored systematical-ly. We have developed a tool set to aid the search of optimal catalysts in the context of phosphine ligands. A virtual library, kra-ken, that is representative of the monodentate P(III)-ligand chemical space was utilized as the basis to represent the discrete lig-ands as continuous variables. Using dimensionality reduction and clustering techniques, we suggested a Phosphine Optimization Screening Set (PHOSS) of 32 commercially available ligands that samples this chemical space completely and evenly. We present the application of this screening set in the identification of active catalyst for various cross-coupling reactions and how well-distributed sampling of the chemical space facilitates identification of active catalysts. Furthermore, we demonstrate how proximi-ty in ligand space can be a useful guide to further explore ligands when very few active catalysts are known.

Accurate prediction of the sensitivity properties of high-energy materials (HEMs) and the study of their decomposition mechanisms are two major focuses within energetics research. Due to the hazards associated with the synthesis and handling of energetic materials, predictive models for HEM sensitivity are of great importance in enabling the safe and efficient development of future HEMs. Traditional predictive modeling of HEM decomposition via machine learning algorithms generally displays limited interpretability, while mechanistic studies of HEMs typically focus on small subsets of structurally analogous compounds lacking generalizability. This study aims to bridge the gap between predictive modeling and computational mechanistic analysis of HEMs, with the goal of providing chemically interpretable models for HEM sensitivity property prediction. Herein, we disclose the use of multivariate linear regression (MLR) modeling for the prediction of the decomposition temperature and impact sensitivity of HEMs. We report an explosophore-based approach to sensitivity property prediction featuring an ensemble of quantum mechanical parameters and computational workflows that enable rapid parameterization and modeling of energetic functional groups. We then employ these methods to accurately predict sensitivity properties of nitrogen-rich tetrazole and azide HEMs. These statistical MLR models are readily interpreted based on the principles of physical organic chemistry, producing structure-property relationships to guide the rational design of new HEMs. Furthermore, we extend our explosophore-based approach to predict the sensitivity properties of HEMs containing multiple, non-equivalent energetic functional groups through the identification of molecular triggers for the bulk decomposition of HEMs. Finally, we showcase the viability of our methods towards ab initio virtual screening of HEMs through predictive modeling of external test sets of tetrazole HEMs using structures and parameters generated exclusively in silico.

ConspectusAt the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate.In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔG⧧ = -RT ln[(S)/(R)], where (S) and (R) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way.Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design.In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

A synthetic method for the palladium-catalyzed cyanation of aryl boronic acids using bench stable and non-toxic N-cyanosuccinimide has been developed. High-throughput experimentation facilitated the screen of 90 different ligands and the resultant statistical data analysis identified that ligand σ–donation, π–acidity and sterics are key drivers that govern yield. Categorization into three ligand groups – monophosphines, bisphosphines and miscellaneous – was performed before the analysis. For the monophosphines, the yield of the reaction increases for strong σ–donating, weak π–accepting ligands, with flexible pendant substituents. For the bisphosphines, the yield predominantly correlates with ligand lability. The applicability of the designed reaction to a wider substrate scope was investigated, showing good functional group tolerance in particular with boronic acids bearing electron-withdrawing substituents. This work outlines the develop-ment of a novel reaction, coupled with a fast and efficient workflow to gain understanding of the optimal ligand properties for the design of improved palladium cross-coupling catalysts.