Prospects for the Computational Design of Bipyridine N,N′-Dioxide Catalysts for Asymmetric Propargylation Reactions

Mechanism and Enantioselectivity in QUINOX-Catalyzed Asymmetric Allylations of Aromatic Aldehydes: Solvent and Substituent Effects

Computational investigations were conducted on the QUINOX-catalyzed asymmetric allylation of aromatic aldehydes with allyltrichlorosilanes. Our calculations provide evidence that the catalytic allylation can follow distinct mechanisms, depending on the solvent employed. In toluene and CH2Cl2, the QUINOX-catalyzed allylation predominantly follows an associative pathway, while in CH3CN, a dissociative pathway becomes more favorable. Noncovalent interactions, such as π-stacking effects for the associative mechanism and CH/π interactions for the dissociative mechanism, play a pivotal role in enantiostereodifferentiation in the asymmetric QUINOX-catalyzed reactions of benzaldehyde. Furthermore, the study unveils how different aldehyde substituents exert differing influences on the catalytic allylation reaction. Specifically, the QUINOX-catalyzed allylation of 4-(trifloromethyl)benzaldehyde displays a strong preference for the associative pathway, yielding excellent results in both yield and enantioselectivity. Conversely, 4-methoxybenzaldehyde tends to favor a dissociative mechanism with reduced yields and enantioselectivity. The mechanistic basis for these remarkable substituent effects on the catalytic allylation reaction was also elucidated. In summary, this research enhances our understanding of the QUINOX-catalyzed asymmetric allylation, shedding light on the role of solvents and substituents in the reaction mechanism and enantioselectivity.

Recent Advances in the Synthesis of Propargyl Derivatives, and Their Application as Synthetic Intermediates and Building Blocks

The propargyl group is a highly versatile moiety whose introduction into small-molecule building blocks opens up new synthetic pathways for further elaboration. The last decade has witnessed remarkable progress in both the synthesis of propargylation agents and their application in the synthesis and functionalization of more elaborate/complex building blocks and intermediates. The goal of this review is to highlight these exciting advances and to underscore their impact.

Theoretical Perspectives in Organocatalysis

It is clear that the field of organocatalysis is continuously expanding during the last decades. With increasing computational capacity and new techniques, computational methods have provided a more economic approach to explore different chemical systems. This review offers a broad yet concise overview of current state-of-the-art studies that have employed novel strategies for catalyst design. The evolution of the all different theoretical approaches most commonly used within organocatalysis is discussed, from the traditional approach, manual-driven, to the most recent one, machine-driven.

Theoretical Design of a Catalyst with Both High Activity and Selectivity in C-H Borylation

Improving both the activity and selectivity of the C-H borylation reaction is currently a hot research topic but also a challenge. In this regard, we suggest a multistrategy combining directing group, coordination unsaturated metal center, and cationic character. Based on Reek's catalyst, we designed a new unsaturated cationic catalyst (1) featuring a directing group for C-H borylation. The calculated free energy barrier of C-H activation is only 7.2 kcal/mol, indicating that the cationic catalyst has higher activity than the original neutral catalyst in this process. Moreover, the comparison suggests that the ortho-C-H borylation pathway is more favorable than the meta and para pathways. The catalyst deconstructions are further performed and prove that the ortho-selectivity is attributed to hydrogen-bonding interactions between the directing group and the substrate, although the ortho site is sterically and electronically unfavorable.

Generative adversarial networks for transition state geometry prediction

This work introduces a novel application of generative adversarial networks (GANs) for the prediction of starting geometries in transition state (TS) searches based on the geometries of reactants and products. The multi-dimensional potential energy space of a chemical reaction often complicates the location of a starting TS geometry, leading to the correct TS combining reactants and products in question. The proposed TS-GAN efficiently maps the space between reactants and products and generates reliable TS guess geometries, and it can be easily combined with any quantum chemical software package performing geometry optimizations. The TS-GAN was trained and applied to generate TS guess structures for typical chemical reactions, such as hydrogen migration, isomerization, and transition metal-catalyzed reactions. The performance of the TS-GAN was directly compared to that of classical approaches, proving its high accuracy and efficiency. The current TS-GAN can be extended to any dataset that contains sufficient chemical reactions for training. The software is freely available for training, experimentation, and prediction at https://github.com/ekraka/TS-GAN.

Reducing Challenges in Organic Synthesis with Stereoselective Hydrogenation and Tandem Catalysis

Tandem catalysis enables the rapid construction of complex architectures from simple building blocks. This Perspective shares our interest in combining stereoselective hydrogenation with transformations such as isomerization, oxidation, and epimerization to solve diverse challenges. We highlight the use of tandem hydrogenation for preparing complex natural products from simple prochiral building blocks and present tandem catalysis involving transfer hydrogenation and dynamic kinetic resolution. Finally, we underline recent breakthroughs and opportunities for asymmetric hydrogenation.

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol-1 were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.

1,1'-Biisoquinolines-Neglected Ligands in the Heterocyclic Diimine Family That Provoke Stereochemical Reflections

1,1′-Biisoquinolines are a class of bidentate nitrogen donor ligands in the heterocyclic diimine family. This review briefly discusses their properties and the key synthetic pathways available and then concentrates upon their coordination behaviour. The ligands are of interest as they exhibit the phenomenon of atropisomerism (hindered rotation about the C1–C1′ bond). A notation for depicting the stereochemistry in coordination compounds containing multiple stereogenic centers is developed. The consequences of the chirality within the ligand on the coordination behaviour is discussed in detail.

Bifunctional squaramides with benzyl-like fragments: analysis of CH⋯π interactions by a multivariate linear regression model and quantum chemical topology

A multivariate linear regression model and quantum chemical topology are used for the quantitative description of non-covalent interactions in the transition state of the Michael addition catalyzed by bifunctional squaramides.

Characterization of Highly Coordinated Allylgermanes: Pivotal Players for Enhanced Nucleophilicity and Stereoselectivity

Allylgermanes with a 4-, 5-, and 6-coordinated germanium center were characterized by X-ray crystallography. Cationic 6-coordinated group 14 allylmetals, which were hitherto assumed to be a transition-state structure of allylations, were successfully isolated. Forming high coordination states significantly enhanced the reactivity of the allylgermanes. In contrast to the 4-coordinated allylgermanes with low reactivity, the highly coordinated species readily reacted with several aldehydes. Furthermore, the high coordination states exerted a significant effect on the E/Z selectivity of allylation depending on external additives. The coordination structure had a dramatic influence on the electronic and steric environments around the Ge center, enabling the geometrically controlled allylation of aldehydes.

The C-H Activation/Bidirecting Group Strategy for Selective Direct Synthesis of Diverse 1,1'-Biisoquinolines

Multidentate ligands are highly important but difficult to access. Herein we disclose an atom- and step-economic synthesis of highly substituted 1,1'-biisoquinolines by a C-H activation/bididirecting group strategy. Through rational design of a bididirecting group to "N-OH + N-OAc", selective unsymmetrical diannulation with two different alkynes in a one-pot reaction has been achieved for the first time to access unsymmetrical biisoquinolines. Moreover, the resultant biisoquinolines show tunable photoluminescence and serve as aggregation-induced emission (AIE) systems.

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand-ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh3)2(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)2(PPh3)2(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H2, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H2-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol-1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol-1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H2-activation barrier were identified.

Application of Q2MM to predictions in stereoselective synthesis

Quantum-Guided Molecular Mechanics (Q2MM) can be used to derive transition state force fields (TSFFs) that allow the fast and accurate predictions of stereoselectivity for a wide range of catalytic enantioselective reactions. The basic ideas behind the derivation of TSFFs using Q2MM are discussed and the steps involved in obtaining a TSFF using the Q2MM code, publically available at github.com/q2mm, are shown. The applicability for a range of reactions, including several non-standard applications of Q2MM, is demonstrated. Future developments of the method are also discussed.

Automated Transition State Theory Calculations for High-Throughput Kinetics

A scarcity of known chemical kinetic parameters leads to the use of many reaction rate estimates, which are not always sufficiently accurate, in the construction of detailed kinetic models. To reduce the reliance on these estimates and improve the accuracy of predictive kinetic models, we have developed a high-throughput, fully automated, reaction rate calculation method, AutoTST. The algorithm integrates automated saddle-point geometry search methods and a canonical transition state theory kinetics calculator. The automatically calculated reaction rates compare favorably to existing estimated rates. Comparison against high level theoretical calculations show the new automated method performs better than rate estimates when the estimate is made by a poor analogy. The method will improve by accounting for internal rotor contributions and by improving methods to determine molecular symmetry.

Automated Quantum Mechanical Predictions of Enantioselectivity in a Rhodium-Catalyzed Asymmetric Hydrogenation

A computational toolkit (AARON: An automated reaction optimizer for new catalysts) is described that automates the density functional theory (DFT) based screening of chiral ligands for transition-metal-catalyzed reactions with well-defined reaction mechanisms but multiple stereocontrolling transition states. This is demonstrated for the Rh-catalyzed asymmetric hydrogenation of (E)-β-aryl-N-acetyl enamides, for which a new C₂ -symmetric phosphorus ligand is designed.

Design and application of a bifunctional organocatalyst guided by electron density topological analyses

Design of a catalyst via the identification of key interactions within the transition state with quantum chemical topology.

Transition State Models for Understanding the Origin of Chiral Induction in Asymmetric Catalysis

In asymmetric catalysis, a chiral catalyst bearing chiral center(s) is employed to impart chirality to developing stereogenic center(s). A rich and diverse set of chiral catalysts is now available in the repertoire of synthetic organic chemistry. The most recent trends point to the emergence of axially chiral catalysts based on binaphthyl motifs, in particular, BINOL-derived phosphoric acids and phosphoramidites. More fascinating ideas took shape in the form of cooperative multicatalysis wherein organo- and transition-metal catalysts are made to work in concert. At the heart of all such manifestations of asymmetric catalysis, classical or contemporary, is the stereodetermining transition state, which holds a perennial control over the stereochemical outcome of the catalytic process. Delving one step deeper, one would find that the origin of the stereoselectivity is delicately dependent on the relative stabilization of one transition state, responsible for the formation of the predominant stereoisomer, over the other transition state for the minor stereoisomer. The most frequently used working hypothesis to rationalize the experimentally observed stereoselectivity places an undue emphasis on steric factors and tends to regard the same as the origin of facial discrimination between the prochiral faces of the reacting partners. In light of the increasing number of asymmetric catalysts that rely on hydrogen bonding as well as other weak non-covalent interactions, it is important to take cognizance of the involvement of such interactions in the sterocontrolling transition states. Modern density functional theories offer a pragmatic and effective way to capture non-covalent interactions in transition states. Aided by the availability of such improved computational tools, it is quite timely that the molecular origin of stereoselectivity is subjected to more intelligible analysis. In this Account, we describe interesting molecular insights into the stereocontrolling transition states of five reaction types, three of which provide access to chiral quaternary carbon atoms. While each reaction has its own utility and interest, the focus of our research has been on the mechanism and the origin of the enantio- and diastereoselectivity. In all of the examples, such as asymmetric diamination, sulfoxidation, allylation, and Wacker-type ring expansion, the role played by non-covalent interactions in the stereocontrolling transition states has been identified as crucial. The transfer of the chiral information from the chiral catalyst to the product is identified as taking place through a series of non-covalent interactions between the catalyst and a given position/orientation of the substrate in the chiral environment offered by the axially chiral catalyst. The molecular insights enunciated herein allude to abundant opportunities for rational modifications of the present generation of catalysts and the choice of substrates in these as well as related families of reactions. It is our intent to propose that the domain of asymmetric catalysis could enjoy additional benefits by having knowledge of the vital stereoelectronic interactions in the stereocontrolling transition states.

Noncovalent Interactions in Organocatalysis and the Prospect of Computational Catalyst Design

Noncovalent interactions are ubiquitous in organic systems, and can play decisive roles in the outcome of asymmetric organocatalytic reactions. Their prevalence, combined with the often subtle line separating favorable dispersion interactions from unfavorable steric interactions, often complicates the identification of the particular noncovalent interactions responsible for stereoselectivity. Ultimately, the stereoselectivity of most organocatalytic reactions hinges on the balance of both favorable and unfavorable noncovalent interactions in the stereocontrolling transition state (TS). In this Account, we provide an overview of our attempts to understand the role of noncovalent interactions in organocatalyzed reactions and to develop new computational tools for organocatalyst design. Following a brief discussion of noncovalent interactions involving aromatic rings and the associated challenges capturing these effects computationally, we summarize two examples of chiral phosphoric acid catalyzed reactions in which noncovalent interactions play pivotal, although somewhat unexpected, roles. In the first, List's catalytic asymmetric Fischer indole reaction, we show that both π-stacking and CH/π interactions of the substrate with the 3,3'-aryl groups of the catalyst impact the stability of the stereocontrolling TS. However, these noncovalent interactions oppose each other, with π-stacking interactions stabilizing the TS leading to one enantiomer and CH/π interactions preferentially stabilizing the competing TS. Ultimately, the CH/π interactions dominate and, when combined with hydrogen bonding interactions, lead to preferential formation of the observed product. In the second example, a series of phosphoric acid catalyzed asymmetric ring openings of meso-epoxides, we show that noncovalent interactions of the substrates with the 3,3'-aryl groups of the catalyst play only an indirect role in stereoselectivity. Instead, the stereoselectivity of these reactions are driven by the electrostatic stabilization of a fleeting partial positive charge in the SN2-like transition state by the chiral electrostatic environment of the phosphoric acid catalyst. Next, we describe our studies of bipyridine N-oxide and N,N'-dioxide catalyzed alkylation reactions. Based on several examples, we demonstrate that there are many potential arrangements of ligands around a hexacoordinate silicon in the stereocontrolling TS, and one must consider all of these in order to identify the lowest-lying TS structures. We also present a model in which electrostatic interactions between a formyl CH group and a chlorine in these TSs underlie the enantioselectivity of these reactions. Finally, we discuss our efforts to develop computational tools for the screening of potential organocatalyst designs, starting in the context of bipyridine N,N'-dioxide catalyzed alkylation reactions. Our new computational tool kit (AARON) has been used to design highly effective catalysts for the asymmetric propargylation of benzaldehyde, and is currently being used to screen catalysts for other reactions. We conclude with our views on the potential roles of computational chemistry in the future of organocatalyst design.

Transition state geometry prediction using molecular group contributions

Geometries of reaction transition states can be predicted accurately using group-contribution scheme with data arranged in a hierarchical tree database.