Multidimensional Correlations in Asymmetric Catalysis through Parameterization of Uncatalyzed Transition States

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.

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction

Density functional theory (DFT) is a powerful tool to model transition state (TS) energies to predict selectivity in chemical synthesis. However, a successful multistep synthesis campaign must navigate energetically narrow differences in pathways that create some limits to rapid and unambiguous application of DFT to these problems. While powerful data science techniques may provide a complementary approach to overcome this problem, doing so with the relatively small data sets that are widespread in organic synthesis presents a significant challenge. Herein, we show that a small data set can be labeled with features from DFT TS calculations to train a feed-forward neural network for predicting enantioselectivity of a Negishi cross-coupling reaction with P-chiral hindered phosphines. This approach to modeling enantioselectivity is compared with conventional approaches, including exclusive use of DFT energies and data science approaches, using features from ligands or ground states with neural network architectures.

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.

Harvesting the fragment-based nature of bifunctional organocatalysts to enhance their activity

Enhancing the activity of bifunctional organocatalysts: a fragment-based approach coupled with activity maps helps identifying better-performing catalytic motifs.

Data Science Meets Physical Organic Chemistry

ConspectusAt the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate.In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔG⧧ = -RT ln[(S)/(R)], where (S) and (R) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way.Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design.In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

Enantioselective α-Arylation of Ketones via a Novel Cu(I)-Bis(phosphine) Dioxide Catalytic System

A novel catalytic system based on copper(I) and chiral bis(phosphine) dioxides is described. This allows the arylation of silyl enol ethers to access enolizable α-arylated ketones in good yields and enantiomeric excess up to 95%. Noncyclic ketones are amenable substrates with this method, which complements other approaches based on palladium catalysis. Optimization of the ligand structure is accomplished via rational design driven by correlation analysis. Preliminary mechanistic hypotheses are also evaluated in order to identify the role of chiral bis(phosphine) dioxides.

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.

Open access in silico tools to predict the ADMET profiling of drug candidates

INTRODUCTION

We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs.

AREAS COVERED

This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design.

EXPERT OPINION

The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.

Enantioselective Hydroamination of Alkenes with Sulfonamides Enabled by Proton-Coupled Electron Transfer

An enantioselective, radical-based method for the intramolecular hydroamination of alkenes with sulfonamides is reported. These reactions are proposed to proceed via N-centered radicals formed by proton-coupled electron transfer (PCET) activation of sulfonamide N-H bonds. Noncovalent interactions between the neutral sulfonamidyl radical and a chiral phosphoric acid generated in the PCET event are hypothesized to serve as the basis for asymmetric induction in a subsequent C-N bond forming step, achieving selectivities of up to 98:2 er. These results offer further support for the ability of noncovalent interactions to enforce stereoselectivity in reactions of transient and highly reactive open-shell intermediates.

Strategies for remote enantiocontrol in chiral gold(iii) complexes applied to catalytic enantioselective γ,δ-Diels-Alder reactions

The use of chiral square planar gold(iii) complexes to access enantioenriched products has rarely been applied in asymmetric catalysis. In this context, we report a mechanistic and synthetic investigation into the use of N-heterocyclic (NHC) gold(iii) complexes in γ,δ-Diels–Alder reactions of 2,4-dienals with cyclopentadiene. The optimal catalyst bearing a unique 2-chloro-1-naphthyl substituent allowed efficient synthesis of functionally rich carbocycles in good yields, diastereo- and enantioselectivities. Transition state and multivariate linear regression (MLR) analysis of both catalyst and substrate trends using molecular descriptors derived from designer parameter acquisition platforms, reveals attractive non-covalent interactions (NCIs) to be key selectivity determinates. These analyses demonstrate that a putative π–π interaction between the substrate proximal double bond and the catalyst aromatic group is an essential feature for high enantioselectivity.

Chiral square planar gold(iii) complexes are employed as catalysts in asymmetric Diels–Alder reactions. The non-covalent interactions responsible for the enantioselectivity are revealed through multivariate linear regression analysis.

Disulfonimides versus Phosphoric Acids in Brønsted Acid Catalysis: The Effect of Weak Hydrogen Bonds and Multiple Acceptors on Complex Structures and Reactivity

In Brønsted acid catalysis, hydrogen bonds play a crucial role for reactivity and selectivity. However, the contribution of weak hydrogen bonds or multiple acceptors has been unclear so far since it is extremely difficult to collect experimental evidence for weak hydrogen bonds. Here, our hydrogen bond and structural access to Brønsted acid/imine complexes was used to analyze BINOL-derived chiral disulfonimide (DSI)/imine complexes. 1H and 15N chemical shifts as well as 1JNH coupling constants revealed for DSI/imine complexes ion pairs with very weak hydrogen bonds. The high acidity of the DSIs leads to a significant weakening of the hydrogen bond as structural anchor. In addition, the five hydrogen bond acceptors of DSI allow an enormous mobility of the imine in the binary DSI complexes. Theoretical calculations predict the hydrogen bonds to oxygen to be energetically less favored; however, their considerable population is corroborated experimentally by NOE and exchange data. Furthermore, an N-alkylimine, which shows excellent reactivity and selectivity in reactions with DSI, reveals an enlarged structural space in complexes with the chiral phosphoric acid TRIP as potential explanation of its reduced reactivity and selectivity. Thus, considering factors such as flexibility and possible hydrogen bond sites is essential for catalyst development in Brønsted acid catalysis.

Internal acidity scale and reactivity evaluation of chiral phosphoric acids with different 3,3'-substituents in Brønsted acid catalysis

NMR H-bond analysis reveals an offset of internal and external acidities of catalysts and allows for a detailed reactivity analysis.

The concept of hydrogen bonding for enhancing substrate binding and controlling selectivity and reactivity is central in catalysis. However, the properties of these key hydrogen bonds and their catalyst-dependent variations are extremely difficult to determine directly by experiments. Here, for the first time the hydrogen bond properties of a whole series of BINOL-derived chiral phosphoric acid (CPA) catalysts in their substrate complexes with various imines were investigated to derive the influence of different 3,3′-substituents on the acidity and reactivity. NMR ¹H and ¹⁵N chemical shifts and ¹J_NH coupling constants of these hydrogen bonds were used to establish an internal acidity scale corroborated by calculations. Deviations from calculated external acidities reveal the importance of intermolecular interactions for this key feature of CPAs. For CPAs with similarly sized binding pockets, a correlation of reactivity and hydrogen bond strengths of the catalyst was found. A catalyst with a very small binding pocket showed significantly reduced reactivities. Therefore, NMR isomerization kinetics, population and chemical shift analyses of binary and ternary complexes as well as reaction kinetics were performed to address the steps of the transfer hydrogenation influencing the overall reaction rate. The results of CPAs with different 3,3′-substituents show a delicate balance between the isomerization and the ternary complex formation to be rate-determining. For CPAs with an identical acidic motif and similar sterics, reactivity and internal acidity correlated inversely. In cases where higher sterical demand within the binary complex hinders the binding of the second substrate, the correlation between acidity and reactivity breaks down.

Light-Driven Intramolecular C-N Cross-Coupling via a Long-Lived Photoactive Photoisomer Complex

Reported herein is a visible-light-driven intramolecular C-N cross-coupling reaction under mild reaction conditions (metal- and photocatalyst-free, at room temperature) via a long-lived photoactive photoisomer complex. This strategy was used to rapidly prepare the N-substituted polycyclic quinazolinone derivatives with a broad substrate scope (>50 examples) and further exploited to synthesize the natural products tryptanthrin, rutaecarpine, and their analogues. The success of gram-scale synthesis and solar-driven transformation, as well as promising tumor-suppressing biological activity, proves the potential of this strategy for practical applications. Mechanistic investigations, including control experiments, DFT calculations, UV-vis spectroscopy, EPR, and X-ray single-crystal structure of the key intermediate, provides insight into the mechanism.

The importance of synthetic chemistry in the pharmaceutical industry

Innovations in synthetic chemistry have enabled the discovery of many breakthrough therapies that have improved human health over the past century. In the face of increasing challenges in the pharmaceutical sector, continued innovation in chemistry is required to drive the discovery of the next wave of medicines. Novel synthetic methods not only unlock access to previously unattainable chemical matter, but also inspire new concepts as to how we design and build chemical matter. We identify some of the most important recent advances in synthetic chemistry as well as opportunities at the interface with partner disciplines that are poised to transform the practice of drug discovery and development.

Catalysis in medicinal chemistry

The advent of transition-metal catalysis (and likewise, bio-catalysis, photoredox-catalysis and organo-catalysis, etc.) promises to greatly increase access to diverse chemical matter in medicinal chemistry, but new catalytic reactions often fail to deliver product in applied synthesis.

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).

Chiral phosphoric acid catalysis: from numbers to insights

Chiral phosphoric acids (CPAs) have emerged as powerful organocatalysts for asymmetric reactions, and applications of computational quantum chemistry have revealed important insights into the activity and selectivity of these catalysts.

Enantioselectivity in CPA-catalyzed Friedel–Crafts reaction of indole and N-tosylimines: a challenge for guiding models

Reaction models derived from theoretical investigations for predicting the enantioselectivity of organocatalytic reactions are very useful, but difficult to formulate for the Friedel–Crafts reaction of indole and N-tosylimines.

Enantioselective Synthesis of N,S-Acetals by an Oxidative Pummerer-Type Transformation using Phase-Transfer Catalysis

Reported is the first enantioselective oxidative Pummerer-type transformation using phase-transfer catalysis to deliver enantioenriched sulfur-bearing heterocycles. This reaction includes the direct oxidation of sulfides to a thionium intermediate, followed by an asymmetric intramolecular nucleophilic addition to form chiral cyclic N,S-acetals with moderate to high enantioselectivites. Deuterium-labelling experiments were performed to identify the stereodiscrimination step of this process. Further analysis of the reaction transition states, by means of multidimensional correlations and DFT calculations, highlight the existence of a set of weak noncovalent interactions between the catalyst and substrate that govern the enantioselectivity of the reaction.