Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling

Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction

Advancements in synthetic organic chemistry are closely related to understanding substrate and catalyst reactivities through detailed mechanistic studies. Traditional mechanistic investigations are labor-intensive and rely on experimental kinetic, thermodynamic, and spectroscopic data. Linear free energy relationships (LFERs), exemplified by Hammett relationships, have long facilitated reactivity prediction despite their inherent limitations when using experimental constants or incorporating comprehensive experimental data. Data-driven modeling, which integrates cheminformatics with machine learning, offers powerful tools for predicting and interpreting mechanisms and effectively handling complex reactivities through multiparameter strategies. This review explores selected examples of data-driven strategies for investigating organic reaction mechanisms. It highlights the evolution and application of computational descriptors for mechanistic inference.

Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔG≠ versus Enantiomeric Excess

With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG≠), grounded firmly in transition-state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG≠, escaping physical limitations, addressing temperature effects, managing non-linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint-, descriptor-, and graph neural network-based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists.

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes

The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure-property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21 631 compounds in tmCAT, 4599 in tmPHOTO, 2782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure-property relationships with machine learning.

Data-driven discovery of active phosphine ligand space for cross-coupling reactions

The design of highly active catalysts is a main theme in organic chemistry, but it still relies heavily on expert experience. Herein, powered by machine-learning global structure exploration, we forge a Metal-Phosphine Catalyst Database (MPCD) with a meticulously designed ligand replacement energy metric, a key descriptor to describe the metal-ligand interactions. It pushes the rational design of organometallic catalysts to a quantitative era, where a ±10 kJ mol-1 window of relative ligand binding strength, a so-called active ligand space (ALS), is identified for highly effective catalyst screening. We highlight the chemistry interpretability and effectiveness of ALS for various C-N, C-C and C-S cross-coupling reactions via a Sabatier-principle-based volcano plot and demonstrate its predictive power in discovering low-cost ligands in catalyzing Suzuki cross-coupling involving aryl chloride. The advent of the MPCD provides a data-driven new route for speeding up organometallic catalysis and other applications.

Facile synthesis of five-membered cyclic RE2P-H iron(0) complexes

The synthesis of five-membered P-heterocyclic iron(0) complexes possessing a P-H unit and two heteroatoms (E = O, N), directly bound to phosphorus, is described. Initial problems to achieve access via "classical" reduction of P-Cl bonds of P-heterocycle complexes, e.g., leading to P-P coupling, could be solved by a "combined two-step" reduction/complexation. The use of K[Fe(CO)4H] not only opened access to such heterocyclic phosphane Fe(CO)4 complexes but also allowed the synthesis of sterically non-shielded 'secondary' heterocyclic phosphane complexes. Spectroscopic and structural parameters are discussed.

Paving the road towards automated homogeneous catalyst design

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

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

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

Bridging the information gap in organic chemical reactions

The varying quality of scientific reports is a well-recognized problem and often results from a lack of standardization and transparency in scientific publications. This situation ultimately leads to prominent complications such as reproducibility issues and the slow uptake of newly developed synthetic methods for pharmaceutical and agrochemical applications. In recent years, various impactful approaches have been advocated to bridge information gaps and to improve the quality of experimental protocols in synthetic organic publications. Here we provide a critical overview of these strategies and present the reader with a versatile set of tools to augment their standard procedures. We formulate eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards. We are aware that this is a substantial effort, but we are convinced that the resulting improved data situation will greatly benefit the progress of chemistry.

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

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

Evidence for Suzuki-Miyaura cross-couplings catalyzed by ligated Pd3-clusters: from cradle to grave

Pdn clusters offer unique selectivity and exploitable reactivity in catalysis. Understanding the behavior of Pdn clusters is thus critical for catalysis, applied synthetic organic chemistry and greener outcomes for precious Pd. The Pd3 cluster, [Pd3(μ-Cl)(μ-PPh2)2(PPh3)3][Cl] (denoted as Pd3Cl2), which exhibits distinctive reactivity, was synthesized and immobilized on a phosphine-functionalized polystyrene resin (denoted as immob-Pd3Cl2). The resultant material served as a tool to study closely the role of Pd3 clusters in a prototypical Suzuki-Miyaura cross-coupling of 4-fluoro-1-bromobenzene and 4-methoxyphenyl boronic acid at varying low Pd ppm concentrations (24, 45, and 68 ppm). Advanced heterogeneity tests such as Hg poisoning and the three-phase test showed that leached mononuclear or nanoparticulate Pd are unlikely to be the major active catalyst species under the reaction conditions tested. EXAFS/XANES analysis from (pre)catalyst and filtered catalysts during and after catalysis has shown the intactness of the triangular structure of the Pd3X2 cluster, with exchange of chloride (X) by bromide during catalytic turnover of bromoarene substrate. This finding is further corroborated by treatment of immob-Pd3Cl2 after catalyzing the Suzuki-Miyaura reaction with excess PPh3, which releases the cluster from the polymer support and so permits direct observation of [Pd3(μ-Br)(μ-PPh2)2(PPh3)3]+ ions by ESI-MS. No evidence is seen for a proposed intermediate in which the bridging halogen on the Pd3 motif is replaced by an aryl group from the organoboronic acid, i.e. formed by a transmetallation-first process. Our findings taken together indicate that the 'Pd3X2' motif is an active catalyst species, which is stabilized by being immobilized, providing a more robust Pd3 cluster catalyst system. Non-immobilized Pd3Cl2 is less stable, as is followed by stepwise XAFS of the non-immobilized Pd3Cl2, which gradually changes to a species consistent with 'Pdx(PPh3)y' type material. Our findings have far-reaching future implications for Pd3 cluster involvement in catalysis, showing that immobilization of Pd3 cluster species offers advantages for rigorous mechanistic examination and applied chemistries.

Ligand-Controlled Regiodivergent Nickel-Catalyzed Hydroaminoalkylation of Unactivated Alkenes

Ligand modulation of transition-metal catalysts to achieve optimal reactivity and selectivity in alkene hydrofunctionalization is a fundamental challenge in synthetic organic chemistry. Hydroaminoalkylation, an atom-economical approach for alkylating amines using alkenes, is particularly significant for amine synthesis in the pharmaceutical, agrochemical, and fine chemical industries. However, the existing methods usually require specific substrate combinations to achieve precise regio- and stereoselectivity, which limits their practical utility. Protocols allowing for regiodivergent hydroaminoalkylation from the same starting materials, controlling both regiochemical and stereochemical outcomes, are currently absent. Herein, we report a ligand-controlled, regiodivergent nickel-catalyzed hydroaminoalkylation of unactivated alkenes with N-sulfonyl amines. The reaction initiates with amine dehydrogenation and involves aza-nickelacycle intermediates. Tritert-butylphosphine promotes branched regioselectivity and syn diastereoselectivity, whereas ethyldiphenylphosphine enables linear selectivity, yielding regioisomers with inverse orientation. Systematic evaluation of diverse monodentate phosphine ligands reveals distinct regioselectivity cliffs, and % Vbur (min), a ligand steric descriptor, was established as a predictive parameter correlating ligand structure to regioselectivity. Computational investigations supported experimental findings, offering mechanistic insights into the origins of regioselectivity. Our method provides an efficient and predictable route for amine synthesis, demonstrating broad substrate scope, excellent tolerance toward various functional groups, and practical advantages. These include the use of readily available starting materials and cost-effective nickel(II) salts as precatalysts.

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here, we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library, ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. We also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

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

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

Machine Learning-Guided Development of Trialkylphosphine Ni(I) Dimers and Applications in Site-Selective Catalysis

Owing to the unknown correlation of a metal's ligand and its resulting preferred speciation in terms of oxidation state, geometry, and nuclearity, a rational design of multinuclear catalysts remains challenging. With the goal to accelerate the identification of suitable ligands that form trialkylphosphine-derived dihalogen-bridged Ni^(I) dimers, we herein employed an assumption-based machine learning approach. The workflow offers guidance in ligand space for a desired speciation without (or only minimal) prior experimental data points. We experimentally verified the predictions and synthesized numerous novel Ni^(I) dimers as well as explored their potential in catalysis. We demonstrate C-I selective arylations of polyhalogenated arenes bearing competing C-Br and C-Cl sites in under 5 min at room temperature using 0.2 mol % of the newly developed dimer, [Ni^(I)(μ-Br)PAd₂(n-Bu)]₂, which is so far unmet with alternative dinuclear or mononuclear Ni or Pd catalysts.

Quantum Mechanical Prediction and Experimental Verification of Au(I)-Catalyzed Substitution-Controlled Syntheses of 1H-Pyrido[4,3-b]indole and Spiro[indoline-3,3'-pyridine] Derivatives

Density functional theory calculations were applied to predict the pathways of gold(I)-catalyzed cycloisomerization of the indole substrates with 1,6-enynes, which were consistent with the ensuing experimental results. The substitution-controlled synthesis led to the formation of 1H-pyrido[4,3-b]indole and spiro[indoline-3,3'-pyridine] derivatives in a tunable way. The reactions had good functional group tolerances, and a possible mechanism was proposed based on the computational and experimental results.

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.

Facile Assembly of Modular-Type Phosphines for Tackling Modern Arylation Processes

This Account presents an overview of a promising collection of phosphine ligands simply made from the modular Fischer indolization process and their applications in modern arylation processes. Using one easily accessible 2-arylindole scaffold, three major phosphino-moiety-positioned ligand series can be readily generated. We have attempted to explore challenging electrophilic and nucleophilic partners for the coupling reaction using the modular ligand tool. For the electrophilic partner study, CM-phos-type ligands, where the phosphino group is located at the 2-arene position of 2-arylindole, allow the successful cross-coupling of aryl mesylates. The CM-phos ligand forms a palladacycle before entering the cross-coupling catalytic cycle. For the nucleophilic partner investigation, the indole C3-positioned phosphines show the first accomplishment of Pd-catalyzed organotitanium nucleophile arylation. Indeed, the aryl-titanium nucleophile undergoes cross-coupling more efficiently than does the organoboron coupling partner in particular cases. Moreover, in the indole C3-positioned phosphine series, the -PPh₂-containing ligands perform better in the highly sterically hindered cross-coupling of aryl chlorides than do ligands containing the -PCy₂ moiety. The catalyst loading can even be reduced to 0.2 mol % Pd for tetra-ortho-substituted biaryl synthesis. This finding offers a new perspective on the next-generation design of phosphine ligands in which the sterically bulky and electron-rich -PR₂ group (R = alkyl) may not be necessary for the cross-coupling of aryl chlorides. In general, we hypothesize that a good balance of steric and electronic properties for entertaining the oxidative addition and reductive elimination steps is crucial to the success of the reaction. For the steric factor, the highly sterically congested -PR₂ group normally favors the reductive elimination, yet we conjecture that this sterically bulky group would serve as an obstacle for the incoming aryl halides. For the electronic factor, the electron rich -PR₂ group is believed to support the oxidative cleavage of the C(Ar)-Cl bond by donating more electron density to the corresponding σ* orbital. Nevertheless, the high electron richness of the -PR₂ group may disfavor the reductive elimination electronically. Overall, an appropriate balance of both electron density and steric bulkiness is suggested to allow the sterically hindered cross-coupling to proceed smoothly. We have found that the -PPh₂-containing ligand is a good starting point for this investigation. The formation of aromatic carbon-carbon (C-C) and carbon-heteroatom (C-X) bonds from aryl chlorides was successfully realized using our proprietary phosphines.In addition to the indole-core-bearing ligand skeleton, we also explored the relevant imidazolyl and carbazolyl phosphines for their unique applications. Interestingly, the carbazolyl ligand, having more flexible C-N axial chirality, displays particular interchangeable Pd-N and Pd-arene coordination, which facilitates both oxidative addition and reductive elimination processes. Moreover, this C-N axially chiral ligand allows the successful asymmetric Suzuki-Miyaura coupling for attaining the most sterically hindered tetra-ortho-substituted biaryls with excellent enantioselectivity. The rationale behind these scientifically interesting findings is presented in detail.

Emerging Trends in Cross-Coupling: Twelve-Electron-Based L1Pd(0) Catalysts, Their Mechanism of Action, and Selected Applications

Monoligated palladium(0) species, L1Pd(0), have emerged as the most active catalytic species in the cross-coupling cycle. Today, there are methods available to generate the highly active but unstable L1Pd(0) catalysts from stable precatalysts. While the size of the ligand plays an important role in the formation of L1Pd(0) during in situ catalysis, the latter can be precisely generated from the precatalyst by various technologies. Computational, kinetic, and experimental studies indicate that all three steps in the catalytic cycle─oxidative addition, transmetalation, and reductive elimination─contain monoligated Pd. The synthesis of precatalysts, their mode of activation, application studies in model systems, as well as in industry are discussed. Ligand parametrization and AI based data science can potentially help predict the facile formation of L1Pd(0) species.

Facile construction of dibenzodioxo[3.3.1]nonanes bearing spirocyclohexadienones via domino [4 + 2] cycloaddition/C(sp3)-H oxidative dehydrogenation coupling reactions

A novel palladium catalyzed homodimerization of ortho-hydroxyphenyl substituted p-QMs has been developed via [4 + 2] cycloaddition/oxidative dehydrogenation coupling domino reactions. An interesting palladium catalyzed intramolecular benzyl C-H oxidation dehydrogenation to form a transannular C(sp³)-O bond was found. This protocol provided an efficient method to construct various dibenzodioxo[3.3.1]nonanes bearing spirocyclohexadienones.