Developing Comprehensive Computational Parameter Sets To Describe the Performance of Pyridine-Oxazoline and Related Ligands

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

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

Selective bioelectronic sensing of pharmacologically relevant quinones using extracellular electron transfer in Lactiplantibacillus plantarum

Redox-active small molecules containing quinone functional groups play important roles as pharmaceuticals, but can be toxic if overdosed. Despite the need for a fast and quantitative method to detect quinone and its derivatives, current sensing strategies are often slow and struggle to differentiate between structural analogs. Leveraging the discovery that microorganisms use certain quinones to perform extracellular electron transfer (EET), we investigated the use of Lactiplantibacillus plantarum as a whole-cell bioelectronic sensor to selectively sense quinone analogs. By tailoring the native EET pathway in L. plantarum, we enabled quantitative quinone sensing of 1,4-dihydroxy-2-naphthoic acid (DHNA) - a gut bifidogenic growth stimulator. We found that L. plantarum could respond to environmental DHNA within seconds, producing concentration-dependent electrical signals. This sensing capacity was robust in different assay media and allowed for continuous monitoring of DHNA concentrations. In a simulated gut environment containing a mixed pool of quinone derivatives, this tailored EET pathway can selectively sense pharmacologically relevant quinone analogs, such as DHNA and menadione, amongst other structurally similar quinone derivatives. We also developed a multivariate model to describe the mechanism behind this selectivity and found a predictable correlation between quinone physiochemical properties and the corresponding electrical signals. Our work presents a new concept to selectively sense quinone using whole-cell bioelectronic sensors and opens the possibility of using probiotic L. plantarum for bioelectronic applications in human health.

Mechanism to model: a physical organic chemistry approach to reaction prediction

The application of mechanistic generalizations is at the core of chemical reaction development and application. These strategies are rooted in physical organic chemistry where mechanistic understandings can be derived from one reaction and applied to explain another. Over time these techniques have evolved from rationalizing observed outcomes to leading experimental design through reaction prediction. In parallel, significant progression in asymmetric organocatalysis has expanded the reach of chiral transfer to new reactions with increased efficiency. However, the complex and diverse catalyst structures applied in this arena have rendered the generalization of asymmetric catalytic processes to be exceptionally challenging. Recognizing this, a portion of our research has been focused on understanding the transferability of chemical observations between similar reactions and exploiting this phenomenon as a platform for prediction. Through these experiences, we have relied on a working knowledge of reaction mechanism to guide the development and application of our models which have been advanced from simple qualitative rules to large statistical models for quantitative predictions. In this feature article, we describe the models acquired to generalize organocatalytic reaction mechanisms and demonstrate their use as a powerful approach for accelerating enantioselective synthesis.

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

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

Nickel-Catalyzed Remote Asymmetric Hydroalkylation of Alkenyl Ethers to Access Ethers of Chiral Dialkyl Carbinols

Site- and enantio-selective alkyl-alkyl bond formation is privileged in the retrosynthetic analysis due to the universality of sp³-hybridized carbon atoms in organic molecules. Herein, we report a nickel-catalyzed remote asymmetric hydroalkylation of alkenyl ethers via synchronous implementation of alkene isomerization and enantioselective C(sp³)-C(sp³) bond formation. Regression analysis of catalyst structure-activity relationships accelerates the rational ligand modification through modular regulation. This reaction has several advantages for synthesizing chiral dialkyl carbinols and their ether derivatives, including the broad substrate scope, good functional group tolerance, excellent regioselectivity (>20:1 regioisomeric ratio), and high enantioselectivity (up to 95% enantiomeric excess).

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

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

Catalytic asymmetric C-H insertion reactions of vinyl carbocations

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(sp³)-H functionalization platform.

Noncovalent Stabilization of Radical Intermediates in the Enantioselective Hydroamination of Alkenes with Sulfonamides

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 N-centered radical and a network of aryl-based NCIs that serve to stabilize the favored diastereomeric transition state. The contributions of these specific aryl-based NCIs to the selectivity were further confirmed through multivariate linear regression analysis by comparing the measured enantioselectivity to computed descriptors. These results highlight the power of NCIs to enable high levels of enantioselectivity in reactions involving uncharged open-shell intermediates and expand our understanding of radical-molecule interactions.

Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.

Multivariate Linear Regression Models to Predict Monomer Poisoning Effect in Ethylene/Polar Monomer Copolymerization Catalyzed by Late Transition Metals

This study combined density functional theory (DFT) calculations and multivariate linear regression (MLR) to analyze the monomer poisoning effect in ethylene/polar monomer copolymerization catalyzed by the Brookhart-type catalysts. The calculation results showed that the poisoning effect of polar monomers with relatively electron-deficient functional groups is weaker, such as ethers, and halogens. On the contrary, polar monomers with electron-rich functional groups (carbonyl, carboxyl, and acyl groups) exert a stronger poisoning effect. In addition, three descriptors that significantly affect the poisoning effect have been proposed on the basis of the multiple linear regression model, viz., the chemical shift of the vinyl carbon atom and heteroatom of polar monomer as well as the metal-X distance in the σ-coordination structure. It is expected that these models could guide the development of efficient catalytic copolymerization system in this field.

Using structure-function relationships to understand the mechanism of phenazine-mediated extracellular electron transfer in Escherichia coli

Phenazines are redox-active nitrogen-containing heterocyclic compounds that can be produced by either bacteria or synthetic approaches. As an electron shuttles (mediators), phenazines are involved in several biological processes facilitating extracellular electron transfer (EET). Therefore, it is of great importance to understand the structural and electronic properties of phenazines that promote EET in microbial electrochemical systems. Our previous study experimentally investigated a phenazine-based library as an exogenous mediator system to facilitate EET in Escherichia coli. Herein, we combine our experimental data with density functional theory (DFT) calculations and multivariate linear regression modeling to understand the structure-function relationships in phenazine-based mediated EET. These calculations demonstrate that the computed redox properties of phenazines in lipophilic environments (e.g., cell membrane) correlate to experimental mediated current densities. Additional DFT-derived molecular properties were considered to develop a predictive model, which could be used in metabolic engineering approaches to introduce phenazines as endogenous mediators into bacteria.

Theoretical Assessment of Dinitrogen Fixation on Carbon Atom

Dinitrogen activation in non-metallic systems has received considerable attention in recent years. Herein, we report the theoretical feasibility of N₂ fixation using aminocarbenes (L) or their anionic derivatives. The molecular descriptors of L and anionic L^- , which affect the interaction of L and anionic L^- with N₂ , were identified through multiple linear regression analysis. Additionally, the electron flow during C-N bond formation was confirmed by performing intrinsic reaction coordination calculations with intrinsic bond orbital analysis for the reaction of anionic L^- with N₂ .

Nickelocene as an Air- and Moisture-Tolerant Precatalyst in the Regioselective Synthesis of Multisubstituted Pyridines

Ni(COD)₂-catalyzed cycloaddition reactions to access pyridines have been extensively studied. However, this catalyst typically requires drying procedures and inert-atmosphere techniques for the reactions. Herein, we report operationally simple nickel(0) catalysis to access substituted pyridines from various nitriles and 1,6-diynes without the aid of air-free techniques. The Ni-Xantphos-based catalytic manifold is tolerant to air, moisture, and heat while promoting the [2 + 2 + 2] cycloaddition reactions with high reaction yields and broad substrate scope. In addition, we disclose that not only the steric effect but also the frontier molecular orbital interactions can play a critical role in determining the regiochemical outcome of nickel-catalyzed [2 + 2 + 2] cycloaddition for the synthesis of substituted pyridines.

Nickel-catalyzed asymmetric reductive cross-coupling of α-chloroesters with (hetero)aryl iodides

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.

Linear Regression Model Development for Analysis of Asymmetric Copper-Bisoxazoline Catalysis

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.

Synthesis of 3-Carbonyl Trisubstituted Furans via Pd-Catalyzed Aerobic Cycloisomerization Reaction: Development and Mechanistic Studies

Herein, we report the synthesis of 3-carbonyl-trisubstituted furans via Pd-catalyzed oxidative cycloisomerization reactions of 2-alkenyl-1,3-dicarbonyl scaffolds, using molecular oxygen as the sole oxidant to regenerate active palladium catalytic species, featuring good functional tolerance and mild reaction conditions. Deep investigation of intermediates and transition states of the reaction mechanism were conducted via experimental and DFT studies, providing a detailed mechanistical profile. The new developed methodology presents a greener alternative to Wacker-type cycloisomerizations and avoids the use of stoichiometric amounts of oxidants and strong acid additives.

Interrogation of 2,2'-Bipyrimidines as Low-Potential Two-Electron Electrolytes

As utilization of renewable energy sources continues to expand, the need for new grid energy storage technologies such as redox flow batteries (RFBs) will be vital. Ultimately, the energy density of a RFB will be dependent on the redox potentials of the respective electrolytes, their solubility, and the number of electrons stored per molecule. With prior literature reports demonstrating the propensity of nitrogen-containing heterocycles to undergo multielectron reduction at low potentials, we focused on the development of a novel electrolyte scaffold based upon a 2,2'-bipyrimidine skeleton. This scaffold is capable of storing two electrons per molecule while also exhibiting a low (∼-2.0 V vs Fc/Fc+) reduction potential. A library of 24 potential bipyrimidine anolytes were synthesized and systematically evaluated to unveil structure-function relationships through computational evaluation. Through analysis of these relationships, it was unveiled that steric interactions disrupting the planarity of the system in the reduced state could be responsible for higher levels of degradation in certain anolytes. The major decomposition pathway was ultimately determined to be protonation of the dianion by solvent, which could be reversed by electrochemical or chemical oxidation. To validate the hypothesis of strain-induced decomposition, two new electrolytes with minimal steric encumbrance were synthesized, evaluated, and found to indeed exhibit higher stability than their sterically hindered counterparts.

Stereoselective Remote Functionalization via Palladium-Catalyzed Redox-Relay Heck Methodologies

Exploration of novel, three-dimensional chemical space is of growing interest in the drug discovery community and with this comes the challenge for synthetic chemists to devise new stereoselective methods to introduce chirality in a rapid and efficient manner. This Minireview provides a timely summary of the development of palladium-catalyzed asymmetric redox-relay Heck-type processes. These reactions represent an important class of transformation for the selective introduction of remote stereocenters, and have risen to prominence over the past decade. Within this Minireview, the vast scope of these transformations will be showcased, alongside applications to pharmaceutically relevant chiral building blocks and drug substances. To complement this overview, a mechanistic summary and discussion of the current limitations of the transformation are presented, followed by an outlook on future areas of investigation.

Cu-Catalyzed Enantioselective Alkylarylation of Vinylarenes Enabled by Chiral Binaphthyl-BOX Hybrid Ligands

Transition-metal-catalyzed radical relay coupling reactions have recently emerged as one of the most powerful methods to achieve difunctionalization of olefins. However, there has been limited success in applying this method to asymmetric catalysis using an effective chiral ligand. Herein we report the Cu-catalyzed enantioselective alkylarylation of vinylarenes using alkylsilyl peroxides as alkyl radical sources. This reaction proceeds under practical reaction conditions and affords chiral 1,1-diarylalkane structures that are found in a variety of bioactive molecules. Notably, a highly enantioselective reaction was accomplished by combining chiral bis(oxazoline) ligands with chiral binaphthyl scaffolds.

Molecular-level insight in supported olefin metathesis catalysts by combining surface organometallic chemistry, high throughput experimentation, and data analysis

A combination of high-throughput experimentation (HTE), surface organometallic chemistry (SOMC) and statistical data analysis provided the platform to analyze in situ silica-grafted Mo imido alkylidene catalysts based on a library of 35 phenols. Overall, these tools allowed for the identification of σ-donor electronic effects and dispersive interactions and as key drivers in a prototypical metathesis reaction, homodimerization of 1-nonene. Univariate and multivariate correlation analysis confirmed the categorization of the catalytic data into two groups, depending on the presence of aryl groups in ortho position of the phenol ligand. The initial activity (TOFin) was predominantly correlated to the σ-donor ability of the aryloxy ligands, while the overall catalytic performance (TON1 h) was mainly dependent on attractive dispersive interactions with the used phenol ligands featuring aryl ortho substituents and, in sharp contrast, repulsive dispersive interactions with phenol free of aryl ortho substituents. This work outlines a fast and efficient workflow of gaining molecular-level insight into supported metathesis catalysts and highlights σ-donor ability and noncovalent interactions as crucial properties for designing active d0 supported metathesis catalysts.

Origin of the Difference in Reactivity between Ir Catalysts for the Borylation of C-H Bonds

A mechanistic study on the origin of the difference in reactivity between Ir catalysts for C-H borylation reactions is reported. Catalytic reactions of B₂pin₂ with a series of substrates that require high temperatures and long reaction times were conducted. These reactions catalyzed by the combination of [Ir(COD)(OMe)]₂ and 3,4,7,8-tetramethylphenanthroline (tmphen) occur in yields that are substantially higher than those of reactions catalyzed by [Ir(COD)(OMe)]₂ and 4,4'-di-tert-butylbipyridine (dtbpy). The electronic properties of Ir catalysts ligated by dtbpy or tmphen and their stoichiometric reactivity were investigated. It was found that a longer lifetime rather than higher reactivity of the catalyst leads to higher yields of reactions catalyzed by Ir-tmphen. The catalyst ligated by dtbpy decomposes principally by dissociation of the ligand and rapid borylation at the positions alpha to nitrogen. Thus, the greater stability of the catalyst containing tmphen results from its greater binding constant.

Developing a Predictive Solubility Model for Monomeric and Oligomeric Cyclopropenium-Based Flow Battery Catholytes

The implementation of redox active organics in nonaqueous redox flow batteries requires the design of molecules that exhibit high solubility (>1 M) in all battery-relevant redox states. Methods for forecasting nonaqueous solubility would be valuable for streamlining the identification of promising structures. Herein we report the development of a workflow to parametrize and predict the solubility of conformationally flexible tris(dialkylamino)cyclopropenium (CP) radical dications. A statistical model is developed through training on monomer species. Ultimately, this model is used to predict new monomeric and dimeric CP derivatives with solubilities of >1 M in acetonitrile in all oxidation states. The most soluble CP monomer exhibits high stability to electrochemical cycling at 1 M in acetonitrile without a supporting electrolyte in a symmetrical flow cell.

Computational Ligand Descriptors for Catalyst Design

Ligands, especially phosphines and carbenes, can play a key role in modifying and controlling homogeneous organometallic catalysts, and they often provide a convenient approach to fine-tuning the performance of known catalysts. The measurable outcomes of such catalyst modifications (yields, rates, selectivity) can be set into context by establishing their relationship to steric and electronic descriptors of ligand properties, and such models can guide the discovery, optimization, and design of catalysts. In this review we present a survey of calculated ligand descriptors, with a particular focus on homogeneous organometallic catalysis. A range of different approaches to calculating steric and electronic parameters are set out and compared, and we have collected descriptors for a range of representative ligand sets, including 30 monodentate phosphorus(III) donor ligands, 23 bidentate P,P-donor ligands, and 30 carbenes, with a view to providing a useful resource for analysis to practitioners. In addition, several case studies of applications of such descriptors, covering both maps and models, have been reviewed, illustrating how descriptor-led studies of catalysis can inform experiments and highlighting good practice for model comparison and evaluation.

An autonomous self-optimizing flow machine for the synthesis of pyridine–oxazoline (PyOX) ligands

A continuous flow synthesis of PyOX ligands has been developed using an autonomous self-optimizing flow reactor.

Origin of stereoselectivity and multidimensional quantitative analysis of ligand effects on yttrium-catalysed polymerization of 2-vinylpyridine

Through a combination of density functional theory (DFT) calculations and multivariate regression analysis, the origin of the stereoselectivity of yttrium-catalysed polymerization of 2-vinylpyridine (2VP) has been investigated.

Computational mapping of redox-switchable metal complexes based on ferrocene derivatives

The properties of redox-switchable metal complexes have been captured with DFT-calculated parameters and processed into a map of chemical space, highlighting the effects of varying metals, donors, linkers and substituents in both accessible ferrocene oxidation states.

Towards operando computational modeling in heterogeneous catalysis

An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.

Enantiodivergent Pd-catalyzed C-C bond formation enabled through ligand parameterization

Despite the enormous potential for the use of stereospecific cross-coupling reactions to rationally manipulate the three-dimensional structure of organic molecules, the factors that control the transfer of stereochemistry in these reactions remain poorly understood. Here we report a mechanistic and synthetic investigation into the use of enantioenriched alkylboron nucleophiles in stereospecific Pd-catalyzed Suzuki cross-coupling reactions. By developing a suite of molecular descriptors of phosphine ligands, we could apply predictive statistical models to select or design distinct ligands that respectively promoted stereoinvertive and stereoretentive cross-coupling reactions. Stereodefined branched structures were thereby accessed through the predictable manipulation of absolute stereochemistry, and a general model for the mechanism of alkylboron transmetallation was proposed.

Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.

Combining traditional 2D and modern physical organic-derived descriptors to predict enhanced enantioselectivity for the key aza-Michael conjugate addition in the synthesis of Prevymis™ (letermovir)

Quantitative structure-activity relationships have an extensive history for optimizing drug candidates, yet they have only recently been applied in reaction development. In this report, the predictive power of multivariate parameterization has been explored toward the optimization of a catalyst promoting an aza-Michael conjugate addition for the asymmetric synthesis of letermovir. A hybrid approach combining 2D QSAR and modern 3D physical organic parameters performed better than either approach in isolation. Using these predictive models, a series of new catalysts were identified, which catalyzed the reaction to provide the desired product in improved enantioselectivity relative to the parent catalyst.

Enantioselective fluorination of homoallylic alcohols enabled by the tuning of non-covalent interactions

The study of the enantioselective fluorination of homoallylic alcohols via chiral anion phase transfer (CAPT) catalysis using an in situ generated directing group is described. Multivariate correlation analysis, including designer π-interaction derived parameters, revealed key structural features affecting the selectivity at the transition state (TS). Interpretation of the parameters found in the model equation highlights the key differences as well as similarities for the reaction of homoallylic and allylic substrates. A similar T-shaped π-interaction was found to occur between the substrate and the catalyst. The tuning of this crucial interaction by identification of the best combination of phosphoric acid catalyst and boronic acid directing group allowed for the development of a methodology to access γ-fluoroalkenols in typically high enantioselectivities (up to 96% ee).

Predictive and mechanistic multivariate linear regression models for reaction development

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis.