Quantifying Structural Effects of Amino Acid Ligands in Pd(II)-Catalyzed Enantioselective C–H Functionalization Reactions

Chiral amino acids: evolution in atroposelective C-H activation

This review covers the journey of chiral amino acids as ligands in atroposelective C-H bond activation/functionalization via transition metal catalysis. Herein, we intend to demonstrate how these chiral amino acids have evolved and flourished in this stimulating field. Unprotected amino acids, mono-N-protected amino acids, and di-N-protected amino acids have been devised for atroposelective C-H activation. In each section, we have briefly discuss the key successes of amino acids in the atroposelective synthesis of biaryls, heterobiaryls, and non-biaryl atropisomers and their advantages in atroposelective C-H activation.

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.

Mechanistic study of enantioselective Pd-catalyzed C(sp3)-H activation of thioethers involving two distinct stereomodels

Enantioselective C(sp³)-H activation has gained considerable attention from the synthetic chemistry community. Despite the intense interest in these reactions, the mechanisms responsible for enantioselection are still vague. In the course of the development of aryl thioether-directed C(sp³)-H arylation, we noticed extreme variation in sensitivity of two substrate classes to substituent effects of ligands and directing groups: whereas 3-pentyl sulfides (prochiral α-center) responded positively to substitution on ligands and directing groups, isobutyl sulfides (prochiral β-center) were entirely insensitive. Quantitative structure selectivity relationship (QSSR) analyses of directing group and ligand substitution and the development of a new class of mono-N-acetyl protected amino anilamide (MPAAn) ligands led to high enantiomeric ratios (up to 99:1) for thioether-directed C(sp³)-H arylation. Key to the realization of this method was the exploitation of transient chirality at sulfur, which relays stereochemical information from the ligand backbone to enantiotopic carbons of the substrate in a rate- and enantio-determining cyclometallation deprotonation. The absolute stereochemistry of the products for these two substrates were revealed to be opposite. DFT evaluation of all possible diastereomeric transition states confirmed initial premises that guided rational ligand and directing group design. The implications of this study will assist in the further development of enantioselective C(sp³)-H activation, namely by highlighting the non-innocence of directing groups, distal steric influences, and the delicate interplay between steric Pauli repulsion and London dispersion in enantioinduction.

Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

The rational design of catalysts remains a challenging endeavor within the broader chemical community owing to the myriad variables that can affect key bond-forming events. Designing selective catalysts for any reaction requires an efficient strategy for discovering predictive structure-activity relationships. Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-pyrrole (C) via a Ti-catalyzed formal [2 + 2 +1] cycloaddition of phenylpropyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space and k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (>90% C) by incorporating 2,6-dimethyl-4-(pyrrolidin-1-yl)pyridine as a ligand. The origin of catalyst selectivity was probed by examining ISPCA variable loadings in combination with DFT modeling, revealing that ligand lability plays an important role in selectivity. A parallel catalyst search using multivariate linear regression (MLR), a popular approach in catalysis informatics, was also conducted in order to compare these strategies in a hypothetical catalyst scouting campaign. ISPCA appears to be more robust and predictive than MLR when sparse training sets are used that are representative of the data available during the early search for an optimal catalyst. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.

From Pd(OAc)2 to Chiral Catalysts: The Discovery and Development of Bifunctional Mono-N-Protected Amino Acid Ligands for Diverse C-H Functionalization Reactions

The functionalization of unactivated carbon-hydrogen bonds is a transformative strategy for the rapid construction of molecular complexity given the ubiquitous presence of C-H bonds in organic molecules. It represents a powerful tool for accelerating the synthesis of natural products and bioactive compounds while reducing the environmental and economic costs of synthesis. At the same time, the ubiquity and strength of C-H bonds also present major challenges toward the realization of transformations that are both highly selective and efficient. The development of practical C-H functionalization reactions has thus remained a compelling yet elusive goal in organic chemistry for over a century.Specifically, the capability to form useful new C-C, C-N, C-O, and C-X bonds via direct C-H functionalization would have wide-ranging impacts in organic synthesis. Palladium is especially attractive as a catalyst for such C-H functionalizations because of the diverse reactivity of intermediate palladium-carbon bonds. Early efforts using cyclopalladation with Pd(OAc)₂ and related salts led to the development of many Pd-catalyzed C-H functionalization reactions. However, Pd(OAc)₂ and other simple Pd salts perform only racemic transformations, which prompted a long search for effective chiral catalysts dating back to the 1970s. Pd salts also have low reactivity with synthetically useful substrates. To address these issues, effective and reliable ligands capable of accelerating and improving the selectivity of Pd-catalyzed C-H functionalizations are needed.In this Account, we highlight the discovery and development of bifunctional mono-N-protected amino acid (MPAA) ligands, which make great strides toward addressing these two challenges. MPAAs enable numerous Pd(II)-catalyzed C(sp²)-H and C(sp³)-H functionalization reactions of synthetically relevant substrates under operationally practical conditions with excellent stereoselectivity when applicable. Mechanistic studies indicate that MPAAs operate as unique bifunctional ligands for C-H activation in which both the carboxylate and amide are coordinated to Pd. The N-acyl group plays an active role in the C-H cleavage step, greatly accelerating C-H activation. The rigid MPAA chelation also results in a predictable transfer of chiral information from a single chiral center on the ligand to the substrate and permits the development of a rational stereomodel to predict the stereochemical outcome of enantioselective reactions.We also describe the application of MPAA-enabled C-H functionalization in total synthesis and provide an outlook for future development in this area. We anticipate that MPAAs and related next-generation ligands will continue to stimulate development in the field of Pd-catalyzed C-H functionalization.

Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry

AIMS

Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.).

BACKGROUND

Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.).

OBJECTIVE

Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures.

METHODS

We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied.

RESULTS

We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series.

CONCLUSION

The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.

Computational Ligand Descriptors for Catalyst Design

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

Palladium-Catalyzed, Enantioselective Formal Cycloaddition between Benzyltriflamides and Allenes: Straightforward Access to Enantioenriched Isoquinolines

Benzyl and allyltriflamides can engage in Pd-catalyzed oxidative (4+2) annulations with allenes, to produce highly valuable tetrahydroisoquinoline or dihydropyridine skeletons. The reaction is especially efficient when carried out in the presence of designed N-protected amino acids as metal ligands. More importantly, using this type of chiral ligands, it is possible to perform desymmetrizing, annulative C-H activations of prochiral diarylmethylphenyl amides, and thus obtain the corresponding isoquinolines with high enantiomeric ratios.

Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-catalyzed thiol addition to N-acylimines.

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 Diaryliodonium Phosphate Enables Light Driven Diastereoselective α-C(sp3)-H Acetalization

C(sp3)-H bond functionalization has emerged as a robust tool enabling rapid construction of molecular complexity from simple building blocks, and the development of asymmetric versions of this reaction creates a powerful methodology to access enantiopure sp3-rich materials. Herein, we report the stereoselective functionalization of C(sp3)-H bonds of cyclic ethers employing a photochemically active diaryliodonium salt in combination with an anionic phase-transfer catalyst. The synthetic strategy outlined herein allows for regio- and stereochemical control in the α-C-H acetalization of furans and pyrans using alcohol nucleophiles, thus providing the ability to control the configuration at the stereogenic exocyclic acetal carbon.

Delineating Physical Organic Parameters in Site-Selective C-H Functionalization of Indoles

Site-selective C-H functionalization is a great challenge in homogeneous transition-metal catalysis. Herein, we present a physical organic approach to delineate the origin of regioselective amidation of N-acylindoles through Ir(III) catalysis. Bulkiness of N-directing groups of indole substrates and electronics of carboxylate additives were identified as two major factors in controlling C2 and C7 selectivity, and their microscopic mechanisms were studied with DFT-based transition state analysis. Computational insights led us to interrogate a linear free energy relationship, and parametrization of molecular determinants enabled the establishment of an intuitive yet robust statistical model that correlates an extensive number of validation data points in high accuracy. This mechanistic investigation eventually allowed the development of a new C2 amidation and alkenylation protocol of indoles, which affords the exclusive functionalization at the C2 position with up to >70:1 selectivity.