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

Enantioselective Pd- and Cu-Catalyzed Hydrofunctionalization of Methylenecyclopropanes via a Distal C-C Bond Cleavage

The enantioselective ring-opening reactions of methylenecyclopropanes (MCPs) involving C-C bond activation via oxidative addition of transition metals have been rarely reported. Here, we disclose a Pd/Cu-catalyzed enantio- and regioselective coupling between cyclic imino esters and MCPs to produce α-allylated 2H-pyrrole derivatives. In this reaction, azomethine ylide formed by a chiral copper catalyst with ketimine ester would serve as a nucleophile to react with activated MCPs via palladium catalysis. This bimetallic catalyst system exhibited a broad substrate scope and high regio- and enantioselectivities.

Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines

Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associated computational cost of descriptor calculation, especially when considering conformational flexibility. Descriptor libraries can be precomputed agnostic of application to reduce the computational burden of data-driven reaction development. However, as one often applies these models to evaluate novel hypothetical structures, it would be ideal to predict the descriptors of compounds on-the-fly. Herein, we report DFT-level descriptor libraries for conformational ensembles of 8528 carboxylic acids and 8172 alkyl amines towards this goal. Employing 2D and 3D graph neural network architectures trained on these libraries culminated in the development of predictive models for molecule-level descriptors, as well as the bond- and atom-level descriptors for the conserved reactive site (carboxylic acid or amine). The predictions were confirmed to be robust for an external validation set of medicinally-relevant carboxylic acids and alkyl amines. Additionally, a retrospective study correlating the rate of amide coupling reactions demonstrated the suitability of the predicted DFT-level descriptors for downstream applications. Ultimately, these models enable high-fidelity predictions for a vast number of potential substrates, greatly increasing accessibility to the field of data-driven reaction development.

Applying statistical modeling strategies to sparse datasets in synthetic chemistry

The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and as a predictive tool for many optimization objectives. This review is aimed as a tutorial for those entering the area of statistical modeling in chemistry. We provide case studies to highlight the considerations and approaches that can be used to successfully analyze datasets in low data regimes, a common situation encountered given the experimental demands of organic chemistry. Statistical modeling hinges on the data (what is being modeled), descriptors (how data are represented), and algorithms (how data are modeled). Herein, we focus on how various reaction outputs (e.g., yield, rate, selectivity, solubility, stability, and turnover number) and data structures (e.g., binned, heavily skewed, and distributed) influence the choice of algorithm used for constructing predictive and chemically insightful statistical models.

Pd-Catalyzed C(sp2)-C(sp3) Suzuki Coupling and Synthesis of Lumacaftor Using Designed Monophosphine Ligands

Novel monophosphine ligands L1 and L2 with a C-P ring were designed and synthesized for the efficient Pd-catalyzed C(sp2)-C(sp3) Suzuki coupling. With 0.5 mol % Pd2dba3 and 2 mol % L1, aryl halides coupled with alkylboronic acids to give the products in up to 99% yield. The aryl thianthrene salt was further applied for late-stage methylation in 97% yield. The active pharmaceutical ingredient lumacaftor was synthesized by aryl-alkyl and aryl-aryl Suzuki coupling reactions using L1 and L2.

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.

Data-Driven Discovery of a New Fluorescent BASHY Dye for Bioimaging

Fluorescent molecules play a crucial role in biomedicine by facilitating the visualization and tracking of biological processes with sensitivity and specificity. However, tailoring their structure to meet the demands of live cell and in vivo imaging presents a significant challenge due to the intricate interplay of factors governing their structural and photophysical properties. In this study, we explored the potential of using multivariate linear free-energy relationships (mLFER) to optimize a multicomponent fluorescent platform. We prepared a small library of 20 fluorescent boronic-acid-derived salicylidenehydrazone (BASHY) complexes using a versatile reaction protocol and characterized their chemical stability in water-containing media. The obtained data served as input for the development of an mLFER model, enabling the prediction of a new BASHY dye and unraveling previously unknown mechanisms governing the stability of this unique platform of fluorescent dyes. The optimized dye was successfully employed in live cell experiments and in zebrafish larvae.

Rapid, Homogenous, B-Alkyl Suzuki-Miyaura Cross-Coupling of Boronic Esters

A rapid, anhydrous Suzuki-Miyaura cross-coupling of alkylboronic esters with aryl halides is described. Parallel experimentation revealed that the combination of AntPhos, an oxaphosphole ligand, neopentyldiol alkylboronic esters, and potassium trimethylsilanolate (TMSOK) enables successful cross-coupling. In general, reactions proceed in under 1 h with good yields and high linear/branched (l/b) selectivities. Crucially, two literature examples which previously took >20 h to reach completion were accomplished in a fraction of the time with the method described herein. Mechanistic studies revealed that the reaction proceeds through a stereoretentive pathway and identified the boronic ester skeleton as a predominant pathway for deleterious protodehalogenation.

Insights into the Synergistic Interplay of Ligand and Base Effects in Palladium-Catalyzed Enantioselectivity Hydrofunctionalization of Dienes

Transition-metal-catalyzed enantioselective C-O bond constructions via hydrofunctionalization involving the use of O-based nucleophiles are an important topic in synthetic chemistry. Herein, density functional theory calculations were conducted to unveil the mechanism and enantioselectivity of Pd-catalyzed asymmetric hydrofunctionalization of conjugated dienes. We found that the base-assisted 4,3-activation model of the ligand-to-ligand hydrogen transfer (LLHT) mechanism is the most preferred one among all the cases, which could be ascribed to the favorable C-H···O interactions and the electrostatic interactions. For the enantioselective C-O bond formation process, the orientation of the substrate in the chiral pocket plays a significant role in controlling the enantioselectivity by contributing different noncovalent interactions. On the basis of the distortion/interaction model and energy decomposition analysis, the distortion energy is identified as the dominant factor controlling the product chemoselectivity. BnOH acts as the substrate, proton shuttle, and stabilizer to facilitate the H-transfer process in both LLHT and the C-O bond formation process. This study provides molecular-level insights into the collaborative effect of the base and P,N-ligand to perform the catalytic activity in the asymmetric hydrofunctionalization of conjugated dienes, which might open a new avenue for designing more efficient base-assisted enantioselective hydrofunctionalization by Pd catalysis.

Advancing Heterogeneous Organic Synthesis With Coordination Chemistry-Empowered Single-Atom Catalysts

For traditional metal complexes, intricate chemistry is required to acquire appropriate ligands for controlling the electron and steric hindrance of metal active centers. Comparatively, the preparation of single-atom catalysts is much easier with more straightforward and effective accesses for the arrangement and control of metal active centers. The presence of coordination atoms or neighboring functional atoms on the supports' surface ensures the stability of metal single-atoms and their interactions with individual metal atoms substantially regulate the performance of metal active centers. Therefore, the collaborative interaction between metal and the surrounding coordination environment enhances the initiation of reaction substrates and the formation and transformation of crucial intermediate compounds, which imparts single-atom catalysts with significant catalytic efficacy, rendering them a valuable framework for investigating the correlation between structure and activity, as well as the reaction mechanism of catalysts in organic reactions. Herein, comprehensive overviews of the coordination interaction for both homogeneous metal complexes and single-atom catalysts in organic reactions are provided. Additionally, reflective conjectures about the advancement of single-atom catalysts in organic synthesis are also proposed to present as a reference for later development.

Theoretical study on ligand conformational self-adaptation for modulating reactivity

The combination of Josiphos-type ligands with Pd catalysts has been instrumental in the rapid development of efficient catalytic processes. We performed density functional theory (DFT) calculations to elucidate the mechanisms and dynamic conformational changes responsible for the reactivity and selectivity observed in Pd-catalysed bicyclization/carbonylation of 1,6-enynes. DFT calculations indicated that the most favourable reaction pathway involves an unusual alkene insertion into the carbon-palladium bond to give high level of enantioselectivity. Here, the reactivity is enhanced by the self-adaptation of the Josiphos-Pd backbone, which allows for two distinct ligand conformations with different steric environments. A half-chair conformation is preferred in migratory insertion, which is both the rate-determining step and the enantioselectivity controlling step. The less hindered steric environment of the half-chair conformation allows for rapid migratory insertion, as confirmed by Surface distance projection maps and IGM analysis. Furthermore, IGM analysis shows that the steric effect between the phenyl group in the ligand and the methyl group on the allene of the substrate is important for enantioselectivity control.

The current utility and future potential of multiborylated alkanes

Organoboron chemistry has become a cornerstone of modern synthetic methodology. Most of these reactions use an organoboron starting material that contains just one C(sp2)-B or C(sp3)-B bond; however, there has been a recent and accelerating trend to prepare multiborylated alkanes that possess two or more C(sp3)-B bonds. This is despite a lack of general reactivity, meaning many of these compounds currently offer limited downstream synthetic value. This Review summarizes recent advances in the exploration of multiborylated alkanes, including a discussion on how these products may be elaborated in further synthetic manipulations.

Molecular complexity as a driving force for the advancement of organic synthesis

The generation of molecular complexity is a primary goal in the field of synthetic chemistry. In the context of retrosynthetic analysis, the concept of molecular complexity is central to identifying productive disconnections and the development of efficient total syntheses. However, this field-defining concept is frequently invoked on an intuitive basis without precise definition or appreciation of its subtleties. Methods for quantifying molecular complexity could prove useful for characterizing the state of synthesis in a more rigorous, reliable and reproducible fashion. As a first step to evaluating the importance of these methods to the state of the field, here we present our perspective on the development of molecular complexity quantification and its implications for chemical synthesis. The extension and application of these methods beyond computer-aided synthesis planning and medicinal chemistry to the traditional practice of 'complex molecule' synthesis could have the potential to unearth new opportunities and more efficient approaches for synthesis.

Illuminating the multiple Lewis acidity of triaryl-boranes via atropisomeric dative adducts

Using the principle that constrained conformational spaces can generate novel and hidden molecular properties, we challenged the commonly held perception that a single-centered Lewis acid reacting with a single-centered Lewis base always forms a single Lewis adduct. Accordingly, the emergence of single-centered but multiple Lewis acidity among sterically hindered and non-symmetric triaryl-boranes is reported. These Lewis acids feature several diastereotopic faces providing multiple binding sites at the same Lewis acid center in the interaction with Lewis bases giving rise to adducts with diastereomeric structures. We demonstrate that with a proper choice of the base, atropisomeric adduct species can be formed that interconvert via the dissociative mechanism rather than conformational isomerism. The existence of this exotic and peculiar molecular phenomenon was experimentally confirmed by the formation of atropisomeric piperidine-borane adducts using state-of-the-art NMR techniques in combination with computational methods.

Selective recognition between aromatics and aliphatics by cage-shaped borates supported by a machine learning approach

Selective recognition between hydrocarbon moieties is a longstanding issue. Although we developed a π-pocket Lewis acid catalyst with high selectivity for aromatic aldehydes over aliphatic ones, a general strategy for catalyst design remains elusive. As an approach that transfers the molecular recognition based on multiple cooperative non-covalent interactions within the π-pocket to a rational catalyst design, herein, we demonstrate Lewis acid catalysts showing improved selectivity through the support of an ensemble algorithm with random forest, Ada Boost, and XG Boost as a machine learning (ML) approach. Using 7963 explanatory variables extracted from model hetero-Diels-Alder reactions, the ensemble algorithm predicted the chemoselectivity of unlearned catalysts. Experiments confirmed the prediction. The proposed catalyst shows the highest selective recognition, reminiscing enzymatic catalytic activity. Additionally, a SHapley Additive exPlanations (SHAP) method suggested that the selectivity originates from the polarizability and three-dimensional size of the catalyst. This insight leads to rational design guidelines for Lewis acid catalysts with dispersion forces.

Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey-Bakshi-Shibata Reduction

We present a case study on how to improve an existing metal-free catalyst for a particularly difficult reaction, namely, the Corey-Bakshi-Shibata (CBS) reduction of butanone, which constitutes the classic and prototypical challenge of being able to differentiate a methyl from an ethyl group. As there are no known strategies on how to address this challenge, we leveraged the power of machine learning by constructing a realistic (for a typical laboratory) small, albeit high-quality, data set of about 100 reactions (run in triplicate) that we used to train a model in combination with a key-intermediate graph (of substrate and catalyst) to predict the differences in Gibbs activation energies ΔΔG‡ of the enantiomeric reaction paths. With the help of this model, we were able to select and subsequently screen a small selection of catalysts and increase the selectivity for the CBS reduction of butanone to 80% enantiomeric excess (ee), the highest possible value achieved to date for this substrate with a metal-free catalyst, thereby also exceeding the best available enzymatic systems (64% ee) and the selectivity with Corey's original catalyst (60% ee). This translates into a >50% improvement in relative ΔG‡ from 0.9 to 1.4 kcal mol-1. We underscore the transformative potential of machine learning in accelerating catalyst design because we rely on a manageable small data set and a key-intermediate graph representing a combination of catalyst and substrate graphs in lieu of a transition-state model. Our results highlight the synergy of synthetic chemistry and data-centric approaches and provide a blueprint for future catalyst optimization.

Simultaneous Stereoinvertive and Stereoselective C(sp3)-C(sp3) Cross-Coupling of Boronic Esters and Allylic Carbonates

With increasing interest in constructing more three-dimensional entities, there has been growing interest in cross-coupling reactions that forge C(sp3)-C(sp3) bonds, which leads to additional challenges as it is not just a more difficult bond to construct but issues of stereocontrol also arise. Herein, we report the stereocontrolled cross-coupling of enantioenriched boronic esters with racemic allylic carbonates enabled by iridium catalysis, leading to the formation of C(sp3)-C(sp3) bonds with single or vicinal stereogenic centers. The method shows broad substrate scope, enabling primary, secondary, and even tertiary boronic esters to be employed, and can be used to prepare any of the four possible stereoisomers of a coupled product with vicinal chiral centers. The new method, which combines the simultaneous enantiospecific reaction of a chiral nucleophile with the enantioselective reaction of a chiral electrophile in a single process, offers a solution for stereodivergent cross-coupling of two C(sp3) fragments.

Orthogonal sp3-Ge/B Bimetallic Modules: Enantioselective Construction and Enantiospecific Cross-Coupling

In this study, a series of enantioenriched sp3-Ge/B bimetallic modules were successfully synthesized via an enantioselective copper-catalyzed hydroboration of carbagermatrane (Ge)-containing alkenes. Orthogonal cross-coupling selectivity under different Pd-catalyzed conditions was achieved in an enantiospecific manner. Notably, the chiral secondary Ge exhibited a remarkable transmetallation ability prior to primary or secondary Bpin. The effectiveness of this Ge/B bimetallic strategy was further demonstrated through the development of new functional small molecules with Aggregation-Induced Emission (AIE) and Circularly Polarized Luminescence (CPL) performance. This represents the first successful example of synthesis of enantioenriched alkylgermanium reagents that permit enantiospecific cross-coupling reactions.

Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge

Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of "hybrid" algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.

Computationally Guided Ligand Discovery from Compound Libraries and Discovery of a New Class of Ligands for Ni-Catalyzed Cross-Electrophile Coupling of Challenging Quinoline Halides

Although screening technology has heavily impacted the fields of metal catalysis and drug discovery, its application to the discovery of new catalyst classes has been limited. The diversity of on- and off-cycle pathways, combined with incomplete mechanistic understanding, means that screens of potential new ligands have thus far been guided by intuitive analysis of the metal binding potential. This has resulted in the discovery of new classes of ligands, but the low hit rates have limited the use of this strategy because large screens require considerable cost and effort. Here, we demonstrate a method to identify promising screening directions via simple and scalable computational and linear regression tools that leads to a substantial improvement in hit rate, enabling the use of smaller screens to find new ligands. The application of this approach to a particular example of Ni-catalyzed cross-electrophile coupling of aryl halides with alkyl halides revealed a previously overlooked trend: reactions with more electron-poor amidine ligands result in a higher yield. Focused screens utilizing this trend were more successful than serendipity-based screening and led to the discovery of two new types of ligands, pyridyl oxadiazoles and pyridyl oximes. These ligands are especially effective for couplings of bromo- and chloroquinolines and isoquinolines, where they are now the state of the art. The simplicity of these models with parameters derived from metal-free ligand structures should make this approach scalable and widely accessible.

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.

Rational Design of Palladium(II) Indenyl and Allyl Complexes Bearing Phosphine and Isocyanide Ancillary Ligands with Promising Antitumor Activity

A new class of palladium-indenyl complexes characterized by the presence of one bulky alkyl isocyanide and one aryl phosphine serving as ancillary ligands has been prepared, presenting high yields and selectivity. All the new products were completely characterized using spectroscopic and spectrometric techniques (NMR, FT-IR, and HRMS), and, for most of them, it was also possible to define their solid-state structures via X-ray diffractometry, revealing that the indenyl fragment always binds to the metal centre with a hapticity intermediate between ƞ3 and ƞ5. A reactivity study carried out using piperidine as a nucleophilic agent proved that the indenyl moiety is the eligible site of attack rather than the isocyanide ligand or the metal centre. All complexes were tested as potential anticancer agents against three ovarian cancer cell lines (A2780, A2780cis, and OVCAR-5) and one breast cancer cell line (MDA-MB-231), displaying comparable activity with respect to cisplatin, which was used as a positive control. Moreover, the similar cytotoxicity observed towards A2780 and A2780cis cells (cisplatin-sensitive and cisplatin-resistant, respectively) suggests that our palladium derivatives presumably act with a mechanism of action different than that of the clinically approved platinum drugs. For comparison, we also synthesized Pd-ƞ3-allyl derivatives, which generally showed a slightly higher activity towards ovarian cancer cells and lower activity towards breast cancer cells with respect to their Pd-indenyl congeners.

Dataset Design for Building Models of Chemical Reactivity

Models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. Here, we discuss the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building.

Visible light-induced palladium-carbon bond weakening in catalytically relevant T-shaped complexes

Triggering one-electron redox processes during palladium catalysis holds the potential to unlock new reaction mechanisms and synthetic methods not previously accessible in the typical two-electron reaction manifolds that dominate palladium catalysis. We report that T-shaped organopalladium(ii) complexes coordinated by a bulky monophosphine, a class of organometallic intermediate featured in a range of contemporary catalytic reactions, undergo blue light-promoted bond weakening leading to mild and efficient homolytic cleavage of strong Pd(ii)-C(sp3) bonds under ambient conditions. The origin of light-triggered radical formation in these systems, which lack an obvious ligand-based chromophore (i.e., π-systems), was investigated using a combination of DFT calculations, photoactinometry, and transient absorption spectroscopy. The available data suggest T-shaped organopalladium(ii) complexes manifest unusual blue light-accessible Pd-to-C(sp3) transition. The quantum efficiency and excited state lifetime of this process were unexpectedly superior compared to a prototypical (α-diimine)Pd(ii) complex featuring a low-lying, ligand-centered LUMO (π*). These results suggest coordinatively-unsaturated organopalladium(ii) compounds, catalysts in myriad catalytic processes, have untapped potential for one-electron reactivity under visible light excitation.

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R2 of 0.712) was obtained on our in-house data. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction.

Stereospecific Transformations of Alkylboronic Esters Enabled by Direct Boron-to-Zinc Transmetalation

Chiral secondary organoboronic esters, when activated with t-butyllithium, are shown to undergo efficient stereoretentive transmetalation with either zinc acetate or zinc chloride. This reaction provides chiral secondary alkylzinc reagents that are configurationally stable under practical experimental conditions. The organozinc compounds were found to engage in stereospecific reactions with difluorocarbene, catalytic cross-couplings with palladium-based catalysts, and trifluoromethylation with a copper(III) complex. Mechanistic and computational studies shed light on the inner workings of the transmetalation event.

Desymmetrization of Vicinal Bis(boronic) Esters by Enantioselective Suzuki-Miyaura Cross-Coupling Reaction

The development of an enantioselective catalytic Suzuki-Miyaura reaction that applies to meso 1,2-diborylcycloalkanes is described. This reaction provides a modular route to enantiomerically enriched substituted carbocycles and heterocycles that retain a synthetically versatile boronic ester. With appropriately constructed substrates, compounds bearing additional stereogenic centers and fully substituted carbon atoms can be generated in a straightforward fashion. Preliminary mechanistic experiments suggest that substrate activation arises from the cooperative effect of vicinal boronic esters during the transmetalation step.

Directed Asymmetric Nickel-Catalyzed Reductive 1,2-Diarylation of Electronically Unactivated Alkenes

Transition-metal catalyzed intermolecular 1,2-diarylation of electronically unactivated alkenes has emerged as an extensive research topic in organic synthesis. However, most examples are mainly limited to terminal alkenes. Furthermore, transition-metal catalyzed asymmetric 1,2-diarylation of unactivated alkenes still remains unsolved and is a formidable challenge. Herein, we describe a highly efficient directed nickel-catalyzed reductive 1,2-diarylation of unactivated internal alkenes with high diastereoselectivities. More importantly, our further effort towards enantioselective 1,2-diarylation of the unactivated terminal and challenging internal alkenes is achieved, furnishing various polyarylalkanes featuring benzylic stereocenters in high yields and with good to high enantioselectivities and high diastereoselectivities. Interestingly, the generation of cationic Ni-catalyst by adding alkali metal fluoride is the key to increased efficiency of this enantioselective reaction.

Stereospecific Acylative Suzuki–Miyaura Cross-Coupling: General Access to Optically Active α-Aryl Carbonyl Compounds

A novel strategy for the stereospecific Pd-catalyzed acylative cross-coupling of enantiomerically enriched alkylboron compounds has been developed. The protocol features an extremely high level of enantiospecificity to allow facile access to synthetically challenging and valuable chiral ketones and carboxylic acid derivatives. The use of a sterically encumbered and electron-rich phosphine ligand proved to be crucial for the success of the reaction. Furthermore, on the basis of experimental and computational studies, a unique mechanism for the transmetalation, assisted by the noncovalent interactions of the C(sp³)-based organoboron reagent, has been identified.

Computer-Driven Development of Ylide Functionalized Phosphines for Palladium-Catalyzed Hiyama Couplings

Palladium-catalyzed couplings of silicon enolates with aryl electrophiles are of great synthetic utility, but often limited to expensive bromide substrates. A comparative experimental study confirmed that none of the established ligand systems allows to couple inexpensive aryl chlorides with α-trimethylsilyl alkylnitriles. In contrast, ylide functionalized phosphines (YPhos) led to encouraging results. A statistical model was developed that correlates the reaction yields with ligand features. It was employed to predict catalyst structures with superior performance. With this cheminformatics approach, YPhos ligands were tailored specifically to the demands of Hiyama couplings. The newly synthesized ligands displayed record-setting activities, enabling the elusive coupling of aryl chlorides with α-trimethylsilyl alkyl nitriles. The preparative utility of the catalyst system was demonstrated by the synthesis of pharmaceutically meaningful α-aryl alkylnitriles, α-arylcarbonyls and biaryls.

TiCl4-Catalyzed Hydroboration of Ketones with Ammonia Borane

Investigation of a variety of Lewis acids for the hydroboration-hydrolysis (reduction) of ketones with amine-boranes has revealed that catalytic (10 mol %) titanium tetrachloride (TiCl₄) in diethyl ether at room temperature immensely accelerates the reaction of ammonia borane. The product alcohols are produced in good to excellent yields within 30 min, even with ketones which typically requires 24 h or longer to reduce under uncatalyzed conditions. Several potentially reactive functionalities are tolerated, and substituted cycloalkanones are reduced diastereoselectively to the thermodynamic product. A deuterium labeling study and ¹¹B NMR analysis of the reaction have been performed to verify the proposed hydroboration mechanism.

Sterically Controlled Late-Stage Functionalization of Bulky Phosphines

The fine-tuning of metal-phosphine-catalyzed reactions relies largely on accessing ever more precisely tuned phosphine ligands by de-novo synthesis. Late-stage C-H functionalization and diversification of commercial phosphines offers rapid access to entire libraries of derivatives based on privileged scaffolds. But existing routes, relying on phosphorus-directed transformations, only yield functionalization of Csp 2 -H bonds in a specific position relative to phosphorus. In contrast to phosphorus-directed strategies, herein we disclose an orthogonal functionalization strategy capable of introducing a range of substituents into previously inaccessible positions on arylphosphines. The strongly coordinating phosphine group acts solely as a bystander in the sterically controlled borylation of bulky phosphines, and the resulting borylated phosphines serve as the supporting ligands for palladium during diversification through phosphine self-assisted Suzuki-Miyaura reactions.

Homogeneity of Supported Single-Atom Active Sites Boosting the Selective Catalytic Transformations

Selective conversion of specific functional groups to desired products is highly important but still challenging in industrial catalytic processes. The adsorption state of surface species is the key factor in modulating the conversion of functional groups, which is correspondingly determined by the uniformity of active sites. However, the non-identical number of metal atoms, geometric shape, and morphology of conventional nanometer-sized metal particles/clusters normally lead to the non-uniform active sites with diverse geometric configurations and local coordination environments, which causes the distinct adsorption states of surface species. Hence, it is highly desired to modulate the homogeneity of the active sites so that the catalytic transformations can be better confined to the desired direction. In this review, the construction strategies and characterization techniques of the uniform active sites that are atomically dispersed on various supports are examined. In particular, their unique behavior in boosting the catalytic performance in various chemical transformations is discussed, including selective hydrogenation, selective oxidation, Suzuki coupling, and other catalytic reactions. In addition, the dynamic evolution of the active sites under reaction conditions and the industrial utilization of the single-atom catalysts are highlighted. Finally, the current challenges and frontiers are identified, and the perspectives on this flourishing field is provided.

Nickel-catalysed diversification of phosphine ligands by formal substitution at phosphorus

We report a diversification strategy that enables the direct substituent exchange of tertiary phosphines. Alkylated phosphonium salts, prepared by standard alkylation of phosphines, are selectively dearylated in a nickel-catalysed process to access alkylphosphine products via a formal substitution at the phosphorus center. The reaction can be used to introduce a wide range of alkyl substituents into both mono- and bisphosphines. We also show that the alkylation and dearylation steps can be conducted in a one-pot sequence, enabling accelerated access to derivatives of the parent ligand. The phosphine products of the reaction are converted in situ to air-stable borane adducts for isolation, and versatile derivatisation reactions of these adducts are demonstrated.

Phosphine substituents can be exchanged by standard alkylation of a phosphine and a subsequent dearylation of the resulting phosphonium salt. A wide variety of alkyl groups can be introduced into both mono- and bidentate ligands using this method.

Copper-Catalyzed Stereospecific Transformations of Alkylboronic Esters

Copper-catalyzed stereospecific cross-couplings of boronic esters are reported. Boron "ate" complexes derived from pinacol boronic esters and tert-butyl lithium undergo stereospecific transmetalation to copper cyanide, followed by coupling with alkynyl bromides, allyl halides, propargylic halides, β-haloenones, hydroxylamine esters, and acyl chlorides. Through this simple transformation, commercially available inexpensive compounds can be employed to convert primary and secondary alkylboronic esters to a wide array of synthetically useful compounds.

Ligand-Based Control of Nickel Catalysts: Switching Chemoselectivity from One-Electron to Two-Electron Pathways in Competing Reactions of 4-Halotetrahydropyrans

Development of nickel-catalyzed transformations would be facilitated by an improved ability to predict which ligands promote and suppress competing mechanisms. We evaluate ligand-based modulation of catalyst preference for one- or two-electron pathways employing 4-halotetrahydropyrans as model substrates that can undergo divergent reaction pathways. Chemoselectivity for one- or two-electron oxidative addition is predicted by ligand class. Phosphine-ligated nickel catalysts favor closed-shell oxidative addition. In contrast, nitrogen-ligated nickel catalysts prefer the one-electron pathway, initiating with halogen atom transfer.

Selective Coupling of 1,2-Bis-Boronic Esters at the more Substituted Site through Visible-Light Activation of Electron Donor-Acceptor Complexes

1,2-Bis-boronic esters are useful synthetic intermediates particularly as the two boronic esters can be selectively functionalized. Usually, the less hindered primary boronic ester reacts, but herein, we report a coupling reaction that enables the reversal of this selectivity. This is achieved through the formation of a boronate complex with an electron-rich aryllithium which, in the presence of an electron-deficient aryl nitrile, leads to the formation of an electron donor-acceptor complex. Following visible-light photoinduced electron transfer, a primary radical is generated which isomerizes to the more stable secondary radical before radical-radical coupling with the arene radical-anion, giving β-aryl primary boronic ester products. The reactions proceed under catalyst-free conditions. This method also allows stereodivergent coupling of cyclic cis-1,2-bis-boronic esters to provide trans-substituted products, complementing the selectivity observed in the Suzuki-Miyaura reaction.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.