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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.

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.

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.

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

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.

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.

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.

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.

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.

An asymmetric reductive cross-coupling of α-chloroesters and (hetero)aryl iodides is reported. This nickel-catalyzed reaction proceeds with a chiral BiOX ligand under mild conditions, affording α-arylesters in good yields and enantioselectivities. The reaction is tolerant of a variety of functional groups, and the resulting products can be converted to pharmaceutically-relevant chiral building blocks. A multivariate linear regression model was developed to quantitatively relate the influence of the α-chloroester substrate and ligand on enantioselectivity., A Ni-catalyzed enantioselective reductive cross-coupling of α-chloroesters and (hetero)aryl iodides is reported. A MLR model was developed to quantitatively relate the influence of the α-chloroester substrate and ligand on enantioselectivity.

Statistical analysis of reaction data with molecular descriptors can enable chemists to identify reactivity cliffs that result from a mechanistic dependence on a specific structural feature. In this study, we develop a broadly applicable and quantitative classification workflow that identifies reactivity cliffs in eleven Ni- and Pd-catalyzed cross-coupling datasets employing monodentate phosphine ligands. A unique ligand steric descriptor, %Vbur (min), is found to divide these datasets into active and inactive regions at a similar threshold value. Organometallic studies demonstrate that this threshold corresponds to the binary outcome of bisligated versus monoligated metal and that %Vbur (min) is a physically meaningful and predictive representation of ligand structure in catalysis. Taken together, we expect that this strategy will be of broad value in mechanistic investigation of structure-reactivity relationships, while providing a means to rationally partition datasets for data-driven modeling.

Herein we report a transformation that generates an array of enantiomerically enriched, alkyl allyl ethers. Cyclic, acyclic, and heteroatom-bearing alkenyl triflates undergo an enantioselective, palladium-catalyzed C–C bond formation with diverse acyclic O-alkyl enol ethers in good yields and excellent enantioselectivities.

We report a data-driven, physical organic approach to the development of new methionine-selective bioconjugation reagents with tunable adduct stabilities. Statistical modeling of structural features described by intrinsic physical organic parameters was applied to the development of a predictive model and to gain insight into features driving the stability of adducts formed from the chemoselective coupling of oxaziridine and methionine thioether partners through Redox Activated Chemical Tagging (ReACT). From these analyses, a correlation between sulfimide stabilities and sulfimide ν (C═O) stretching frequencies was revealed. We exploited the rational gains in adduct stability exposed by this analysis to achieve the design and synthesis of a bis-oxaziridine reagent for peptide stapling. Indeed, we observed that a macrocyclic peptide formed by ReACT stapling at methionine exhibited improved uptake into live cells compared to an unstapled congener, highlighting the potential utility of this unique chemical tool for thioether modification. This work provides a template for the broader use of data-driven approaches to bioconjugation chemistry and other chemical biology applications.

The carbon-fluorine bond engenders distinctive physicochemical properties and significant changes to general reactivity, which have impacted molecular design in material science, agrochemicals, and pharmaceutical research. These unique features are generally derived from fluorine having the highest electronegativity coupled to an enhanced bond strength of the C‒F bond compared to a C‒H bond. As a result, C‒F bonds are often leveraged as replacements for C‒H bonds when the latter presents a liability at a saturated (sp(3)) carbon centre especially for benzylic C‒H bonds prone to metabolic oxidation. In this context, the development of catalytic, enantioselective methods to set stereocenters containing a benzylic C‒F bond has been an important and rapidly evolving goal in synthetic chemistry. There have been notable advances enabling the construction of secondary stereocenters containing both a C‒F and a C‒H bond on the same carbon. In contrast, there are significantly fewer synthetic strategies defined for accessing stereocenters that incorporate a tertiary C‒F bond, especially those remote from pre-existing activating groups. Herein, we report a general method that establishes C‒F tertiary, benzylic stereocenters by forging a C‒C bond via a Pd-catalyzed enantioselective Heck reaction of acyclic alkenyl fluorides with arylboronic acids. This method provides a platform to rapidly incorporate significant functionality about the benzylic tertiary fluoride by virtue of the diversity of both reaction partners as well as the ability to install the stereocenters remotely from pre-existing functional groups.

Summary: When faced with unfamiliar reaction space, synthetic chemists typically apply reported conditions (reagents, catalyst, solvent, additives) from closely-related reactions to new substrate types. Unfortunately, this approach often fails due to subtle, albeit important, differences in reaction requirements. Consequently, a significant goal in synthetic chemistry is the ability to transfer chemical observations from one reaction to another, quantitatively. Here, we present such a platform by developing a holistic, data-driven workflow for deriving statistical models for one set of reactions that can be applied to predict out-of-sample examples. As a validating case study, published enantioselectivity data sets that employ BINOL-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines were combined and statistical models developed. These models reveal the general interactions imparting asymmetric induction and allow the quantitative transfer of this information to new reaction components. The disclosed techniques create opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development., Methods Summary: After the database of the reactions is constructed, the experimental output, enantiomeric ratios, were mathematically modelled through linear regression techniques to reveal which of the proposed parameters allow for the prediction of new outcomes. The detailed acquisition of parameters can be found in the Supplementary Information and the descriptor tables attached as an accompanying spreadsheet. The models produced were evaluated for their goodness of fit, R2, and their robustness is demonstrated by external validations’ goodness of fit, predR2. The nearer the R2 and slope values are to one (indicating a tight, one-to-one correlation between predicted and measured outcomes) and the nearer the intercept is to zero (indicating minimal systematic error), the more robust the model. Potential models were refined through number of parameters, because this allows for a mechanistically informative interrogation and cross-validation scores. Leave one reaction out (LORO) analysis was performed to probe general mechanistic principles, which provides the basis for mechanistic transfer of experimental observations and tested further by predicting out-of-sample.

High-throughput experimentation and multivariate modeling allow identification of noncovalent interactions (NCIs) in monoaryloxy-pyrrolide Mo imido alkylidene metathesis catalysts prepared in situ as a key driver for high activity in a representative metathesis reaction (homodimerization of 1-nonene). Statistical univariate and multivariate modeling categorizes catalytic data from 35 phenolic ligands into two groups, depending on the substitution in the ortho position of the phenol ligand. The catalytic activity descriptor TON1h correlates predominantly with attractive NCIs when phenols bear ortho aryl substituents and, conversely, with repulsive NCIs when the phenol has no aryl ortho substituents. Energetic span analysis is deployed to relate the observed NCI and the cycloreversion metathesis step such that aryloxide ligands with no ortho aryls mainly impact the energy of metallacyclobutane intermediates (SP/TBP isomers), whereas aryloxides with pendant ortho aryls influence the transition state energy for the cycloreversion step. While the electronic effects from the aryloxide ligands also play a role, our work outlines how NCIs may be exploited for the design of improved d0 metathesis catalysts.

The development of an intermolecular and enantioselective aza-Wacker reaction is described. Using indoles as the N-source anda selection of alkenols as the coupling partnersselective β-hydride elimination toward the alcohol was achieved. This strategy preserves the newly formed stereocenter by preventing the formation of traditionally observed enamine products. Allylic and homoallylic alcohols with a variety of functional groups are compatible with the reaction in high enantioselectivity. Isotopic-labeling experiments support a syn amino-palladation mechanism for this new class of aza-Wacker reactions.

Catalysts that control stereochemistry are prized tools in chemical synthesis. When an effective catalyst is found, it is often explored for other types of reactions, frequently under the auspices of different mechanisms. As successes mount, a unique catalyst scaffold may become viewed as “privileged”. However, the mechanistic hallmarks of privileged catalysts are not easily enumerated, nor readily generalized to genuinely different classes of reactions or substrates. We explored the concept of scaffold uniqueness with two catalyst types for an unusual atropisomer-selective cyclodehydration: (a) C(2)-symmetric chiral phosphoric acids, and (b) phosphothreonine-embedded, peptidic phosphoric acids. Pragmatically, both catalyst scaffolds proved fertile for enantioselective/atroposelective cyclodehydrations. Mechanistic studies revealed that the determinants of often equivalent and high atroposelectivity are different for the two catalyst classes. A data-descriptive classification of these asymmetric catalysts reveals an increasingly broad set of catalyst chemotypes, operating with different mechanistic features, that creates new opportunities for broad and complementary application of catalyst scaffolds in diverse substrate space.

A range of electroanalytical tools can be applied to studying redox reactions, probing key mechanistic questions in synthetic chemistry., Monitoring reactive intermediates can provide vital information in the study of synthetic reaction mechanisms, enabling the design of new catalysts and methods. Many synthetic transformations are centred on the alteration of oxidation states, but these redox processes frequently pass through intermediates with short life-times, making their study challenging. A variety of electroanalytical tools can be utilised to investigate these redox-active intermediates: from voltammetry to in situ spectroelectrochemistry and scanning electrochemical microscopy. This perspective provides an overview of these tools, with examples of both electrochemically-initiated processes and monitoring redox-active intermediates formed chemically in solution. The article is designed to introduce synthetic organic and organometallic chemists to electroanalytical techniques and their use in probing key mechanistic questions.

A regioselective and enantioselective palladium-catalyzed relay Heck alkenylation of alkenylbenzene derivatives to construct remote stereocenters is disclosed. Various β-substituted styrenes were readily obtained in moderate yields with good to excellent levels of enantioselectivity. This strategy provides rapid access to enantioenriched δ, e, ζ, and η-alkenyl aryl compounds from simple starting materials. Mechanistic studies suggest that termination of the relay reaction is controlled by affinity of the arene for the Pd complex during migration.

Catalytic carbonyl-olefin metathesis reactions have recently been developed as a powerful tool for carbon-carbon bondformation. However, currently available synthetic protocols rely exclusively on aryl ketone substrates while the corresponding aliphatic analogs remain elusive. We herein report the development of Lewis acid-catalyzed carbonyl-olefin ring-closing metathesis reactions for aliphatic ketones. Mechanistic investigations are consistent with a distinct mode of activation relying on the in situ formation of a homobimetallic singly-bridged iron(III)-dimer as the active catalytic species. These “superelectrophiles” function as more powerful Lewis acid catalysts that form upon association of individual iron(III)-monomers. While this mode of Lewis acid activation has previously been postulated to exist, it has not yet been applied in a catalytic setting. The insights presented are expected to enable further advancement in Lewis acid catalysis by building upon the activation principle of “superelectrophiles” and broaden the current scope of catalytic carbonyl-olefin metathesis reactions.

We present data demonstrating the natural product mimic, zinaamidole A (ZNA), is a modulator of metal ion homeostasis causing cancer-selective cell death by specifically inducing cellular Zn(2+)-uptake in transformed cells. ZNA’s cancer selectivity was evaluated using metastatic, patient-derived breast cancer cells, established human breast cancer cell lines, and three-dimensional organoid models derived from normal and transformed mouse mammary glands. Structural analysis of ZNA demonstrated that the compound interacts with zinc through the N(2)-acyl-2-aminoimidazole core. Combination treatment with ZnSO(4) strongly potentiated ZNA’s cancer-specific cell death mechanism, an effect that was not observed with other transition metals. We show that Zn(2+)-dyshomeostasis induced by ZNA is unique and markedly more selective than other known Zn(2+)interacting compounds such as clioquinol. The in vivo bioactivity of ZNA was also assessed and revealed that tumor-bearing mice treated with ZNA had improved survival outcomes. Collectively, these data demonstrate that the N(2)-acyl-2aminoimidazole core of ZNA represents a powerful chemotype to induce cell death in cancer cells concurrently with a disruption in zinc homeostasis.

The staying power of electron-poor ligands The venerable Suzuki coupling reaction originally used palladium to pair up unsaturated carbon centers. The protocol has been widely extended to chiral saturated alkyl carbons, but control over product stereochemistry is a pressing challenge. Zhao et al. systematically studied how the properties of the phosphine ligands that are coordinated to the catalyst influence the stereochemical outcome. Certain electron-withdrawing phosphines favored retention of the initial configuration in chiral alkyltrifluoroborate reactants. Conversely, bulky electron-rich phosphines lead to inverted configurations in the products. Science , this issue p. 670

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.