Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines

Photocatalytic Enantioselective Radical Cascade Multicomponent Minisci Reaction of β-Carbolines Using Diazo Compounds as Radical Precursors

Here, a photocatalytic asymmetric multicomponent cascade Minisci reaction of β-carbolines with enamides and diazo compounds is reported, enabling an effective enantioselective radical C─H functionalization of β-carbolines with high yields and enantioselectivity (up to 83% yield and 95% ee). This enantioselective multicomponent Minisci protocol exhibits step economy, high chemo-/enantio-selective control, and good functional group tolerance, allowing access to a variety of valuable chiral β-carbolines. Notably, diazo compounds are suitable radical precursors in enantioselective cascade radical reactions. Moreover, the efficiency and practicality of this approach are demonstrated by the asymmetric synthesis of bioactive compounds and natural products.

Regioselectivity Switch of α-Amino Acid-Derived Esters and MBH Carbonates for the Synthesis of Allyl-Substituted Azlactones

Allylic azlactones are greatly significant in terms of potential bioactivities and synthetic applications. Owing to the burgeoning interest of the pharmaceutical industry in α-amino acid derivatives, discovering strategies for the synthesis of allylic azlactones is important. Herein, we establish a transition-metal-free regioselectivity switch of α-amino acid-derived esters and MBH carbonates, which exhibits broad reaction scope and good reaction yields. Control reactions indicate that both base and solvent are important for regioselectivity.

Enantioselective synthesis of 3-(N-indolyl)quinolines containing axial and central chiralities

Quinoline and indole are important core structures in biologically active compounds and materials. Atropisomeric biaryls consisting of quinoline and indole are a unique class of axially chiral molecules. We report herein enantioselective synthesis of 3-(N-indolyl)quinolines having both C-N axial chirality and carbon central chirality by a photoredox Minisci-type addition reaction catalyzed by a chiral lithium phosphate/Ir-photoredox complex. The catalytic system enabled access to a unique class of 3-(N-indolyl)quinolines with high chemo-, regio-, and stereoselectivities in good yields through the appropriate choice of an acid catalyst and a photocatalyst. This is the first example of the synthesis of 3-(N-indolyl)quinoline atropisomers in a highly enantioselective manner.

Fragment contribution models for predicting skin permeability using HuskinDB

Mathematical models to predict skin permeation tend to be based on animal derived experimental data as well as knowing physicochemical properties of the compound under investigation, such as molecular volume, polarity and lipophilicity. This paper presents a strikingly contrasting model to predict permeability, formed entirely from simple chemical fragment (functional group) data and a recently released, freely accessible human (i.e. non-animal) skin permeation database, known as the 'Human Skin Database - HuskinDB'. Data from within the database allowed development of several fragment-based models, each including a calculable effect for all of the most commonly encountered functional groups present in compounds within the database. The developed models can be applied to predict human skin permeability (logKp) for any compound containing one or more of the functional groups analysed from the dataset with no need to know any other physicochemical properties, solely the type and number of each functional group within the chemical structure itself. This approach simplifies mathematical prediction of permeability for compounds with similar properties to those used in this study.

α-Amino Acids: An Emerging Versatile Synthon in Visible Light-Driven Decarboxylative Transformations

α-Amino acids have been widely recognized as environmental-benign and non-fossil carbon sources both in biological and synthetic chemistry. In recent years, with the remarkable development of visible-light photocatalysis in organic synthesis, α-amino acid and its derivatives have received tremendous attention as radical precursors via photocatalyzed decarboxylation, thus realizing diverse aminoalkylated transformations or constructions of novel N-bearing heterocyclic motifs by taking advantage of N-atoms from α-amino acid. This review aims to provide a comprehensive update on the recent exploitation of α-amino acids in visible light photocatalysis, with particular emphasis on the types of α-amino acids employed and their distinct mechanisms applied wherein.

Mechanism to model: a physical organic chemistry approach to reaction prediction

The application of mechanistic generalizations is at the core of chemical reaction development and application. These strategies are rooted in physical organic chemistry where mechanistic understandings can be derived from one reaction and applied to explain another. Over time these techniques have evolved from rationalizing observed outcomes to leading experimental design through reaction prediction. In parallel, significant progression in asymmetric organocatalysis has expanded the reach of chiral transfer to new reactions with increased efficiency. However, the complex and diverse catalyst structures applied in this arena have rendered the generalization of asymmetric catalytic processes to be exceptionally challenging. Recognizing this, a portion of our research has been focused on understanding the transferability of chemical observations between similar reactions and exploiting this phenomenon as a platform for prediction. Through these experiences, we have relied on a working knowledge of reaction mechanism to guide the development and application of our models which have been advanced from simple qualitative rules to large statistical models for quantitative predictions. In this feature article, we describe the models acquired to generalize organocatalytic reaction mechanisms and demonstrate their use as a powerful approach for accelerating enantioselective synthesis.

Electrocatalyzed direct arene alkenylations without directing groups for selective late-stage drug diversification

Electrooxidation has emerged as an increasingly viable platform in molecular syntheses that can avoid stoichiometric chemical redox agents. Despite major progress in electrochemical C-H activations, these arene functionalizations generally require directing groups to enable the C-H activation. The installation and removal of these directing groups call for additional synthesis steps, which jeopardizes the inherent efficacy of the electrochemical C-H activation approach, leading to undesired waste with reduced step and atom economy. In sharp contrast, herein we present palladium-electrochemical C-H olefinations of simple arenes devoid of exogenous directing groups. The robust electrocatalysis protocol proved amenable to a wide range of both electron-rich and electron-deficient arenes under exceedingly mild reaction conditions, avoiding chemical oxidants. This study points to an interesting approach of two electrochemical transformations for the success of outstanding levels of position-selectivities in direct olefinations of electron-rich anisoles. A physical organic parameter-based machine learning model was developed to predict position-selectivity in electrochemical C-H olefinations. Furthermore, late-stage functionalizations set the stage for the direct C-H olefinations of structurally complex pharmaceutically relevant compounds, thereby avoiding protection and directing group manipulations.

Photocatalytic enantioselective Minisci reaction of β-carbolines and application to natural product synthesis

A highly efficient enantioselective direct C-H functionalization of β-carbolines via a Minisci-type radical process under a photo-redox and chiral phosphoric acid cooperative catalytic system has been disclosed. Through this protocol, a wide range of C1 aminoalkylated β-carbolines were constructed directly with high levels of enantioselectivities from readily available β-carbolines and alanine-derived redox-active esters. This transformation allows straightforward access to highly valuable enantioenriched β-carbolines, which are an intriguing structural motif in valuable natural products and synthetic bio-active compounds. This protocol has been utilized as a highly efficient synthetic strategy for the concise asymmetric total synthesis of marine alkaloids eudistomin X, (+)-eudistomidin B and (+)-eudistomidin I.

Enantioselective Giese Additions of Prochiral α-Amino Radicals

Amines featuring an adjacent stereocenter are important building blocks, and recent years have seen remarkable growth in methods forming these via prochiral α-amino radical intermediates. However, very few can exert control over the newly formed stereocenter. We disclose a strategy to overcome this in the context of one of the most widely used radical carbon-carbon bond forming reactions, the Giese reaction. Incorporation of a removable basic heteroarene into the substrate enables a network of attractive noncovalent interactions between a phosphoric acid catalyst, the subsequently formed α-amino radical, and the Giese acceptor, allowing the catalyst to exert control during the C-C bond forming step. Deprotection of the products leads to analogues of γ-aminobutyric acid. We anticipate that this strategy will be applicable to other asymmetric radical transformations in which catalyst control is presently challenging.

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.

Strategies That Utilize Ion Pairing Interactions to Exert Selectivity Control in the Functionalization of C-H Bonds

Electrostatic attraction between two groups of opposite charge, typically known as ion-pairing, offers unique opportunities for the design of systems to enable selectivity control in chemical reactions. Catalysis using noncovalent interactions is an established and vibrant research area, but it is noticeable that hydrogen bonding interactions are still the main interaction of choice in system design. Opposite charges experience the powerful force of Coulombic attraction and have the ability to exert fundamental influence on the outcome of reactions that involve charged reagents, intermediates or catalysts. In this Perspective, we will examine how ion-pairing interactions have been used to control selectivity in C-H bond functionalization processes. This broad class of reactions provides an interesting and thought-provoking lens through which to examine the application of ion-pairing design strategies because it is one that encompasses great mechanistic diversity, poses significant selectivity challenges, and perhaps most importantly is of immense interest to synthetic chemists in both industry and academia. We survey reactions that proceed via radical and ionic mechanisms alongside those that involve transition metal catalysis and will deal with control of site-selectivity and enantioselectivity. We anticipate that as this emerging area develops, it will become an ever-more important design strategy for selectivity control.

Catalytic Enantioselective Reductive Cross Coupling of Electron-Deficient Olefins

We report an enantioselective reductive cross coupling of electron-deficient olefins. Using a visible-light-driven cooperative photoredox and chiral Brønsted acid-catalyzed reaction with a Hantzsch ester as the terminal reductant, various cyclic and acyclic enones with 2-vinylpyridines were converted in high yields (up to 93%) to a wide range of enantioenriched pyridine derivatives featuring diverse γ-tertiary carbon stereocenters with good to excellent enantioselectivities (up to >99% ee).

Hydrogen Atom Transfer Driven Enantioselective Minisci Reaction of Alcohols

Catalytic enantioselective Minisci reactions have recently been developed but all instances so far utilize α-amino radical coupling partners. We report a substantial evolution of the enantioselective Minisci reaction that enables α-hydroxy radicals to be used, providing valuable enantioenriched secondary alcohol products. This is achieved through the direct oxidative coupling of two C-H bonds on simple alcohol and pyridine partners through a hydrogen atom transfer (HAT)-driven approach: a challenging process to achieve due to the numerous side reactions that can occur. Our approach is highly regioselective as well as highly enantioselective. Dicumyl peroxide, upon irradiation with 390 nm light, serves as both HAT reagent and oxidant whilst selectivity is controlled by use of a chiral phosphoric acid catalyst. Computational and experimental evidence provide mechanistic insight as to the origin of selectivity, revealing a stereodetermining deprotonation step distinct from the analogous reaction of amide-containing substrates.

Evaluation guidelines for machine learning tools in the chemical sciences

Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generation, improvement and/or ordering of research hypotheses. Despite the overarching applicability of ML workflows, one usually finds diverse evaluation study designs. The current heterogeneity in evaluation techniques and metrics leads to difficulty in (or the impossibility of) comparing and assessing the relevance of new algorithms. Ultimately, this may delay the digitalization of chemistry at scale and confuse method developers, experimentalists, reviewers and journal editors. In this Perspective, we critically discuss a set of method development and evaluation guidelines for different types of ML-based publications, emphasizing supervised learning. We provide a diverse collection of examples from various authors and disciplines in chemistry. While taking into account varying accessibility across research groups, our recommendations focus on reporting completeness and standardizing comparisons between tools. We aim to further contribute to improved ML transparency and credibility by suggesting a checklist of retro-/prospective tests and dissecting their importance. We envisage that the wide adoption and continuous update of best practices will encourage an informed use of ML on real-world problems related to the chemical sciences.

Catalytic Asymmetric Reductive Azaarylation of Olefins via Enantioselective Radical Coupling

Visible-light-driven photocatalytic reductive azaarylation has been widely used to construct the important imine-containing azaarene derivatives. In addition to the direct use of various commercially available cyanoazaarenes as feedstocks, the synthetic advantages include precise regioselectivity, high efficiency, mild reaction conditions, and good functional group tolerance. However, although many efficient reductive azaarylation methods have been established, the example of an enantioselective manner is still unmet, which most likely can be ascribed to the highly reactive radical coupling as the key step of forming stereocenters. Exploring the feasibility of enantiocontrol thus constitutes an attractive but highly challenging task. Here, we demonstrate that chiral hydrogen-bonding/photosensitizer catalysis is a viable platform as it enables the realization of the first enantioselective manifold. A variety of acyclic and cyclic enones as the reaction partners are compatible with the dual catalyst system, leading to a wide array of valuable enantioenriched azaarene variants with high yields and ees. Regulating the types of chiral catalysts represents one of the important manners to success, in which several readily accessible Cinchona alkaloid-derived bifunctional catalysts are introduced in asymmetric photochemical reactions.

A predictive and mechanistic statistical modelling workflow for improving decision making in organic synthesis and catalysis

The application of multivariate linear regression models has been widely utilized as a strategy to streamline the reaction optimization process. While these tools likely provide relatively safe predictions, embedding a method for forecasting the probability of achieving the desired reaction outcome would be valuable for streamlining the identification of promising structures with the best chance of success. Herein, we present a workflow that predicts the probability that a reaction will be successful and is easy and quick to apply. We show that this probabilistic framework can effectively differentiate between predictions often indistinguishable by multivariate linear regression analysis. Moreover, these techniques can enhance the development of mechanistically informative correlations by producing more direct pathways for molecular development and design. Overall, we anticipate this protocol will be generally applicable and useful for accelerating successful chemical discovery.

Catalytic Asymmetric Construction of Axially and Centrally Chiral Heterobiaryls by Minisci Reaction

Axially chiral biaryls and heterobiaryls constitute the most represented subclass of atropisomers with prevalence in natural products, bioactive compounds, privileged chiral ligand/catalysts, and optically pure materials. Despite many ionic protocols for their construction, radical-based variants represent another highly desirable and intriguing strategy but are far less developed. Moreover, efficient synthesis of axially chiral heterobiaryl molecules, especially ones having multiple heteroatoms and other types of chiral elements, through radical routes remains extremely limited. We herein disclose the first catalytic asymmetric, metal-free construction of axially and centrally chiral heterobiaryls by Minisci reaction of 5-arylpyrimidines and α-amino acid-derived redox-active esters. This is enabled by the use of 4CzIPN as an organic photoredox catalyst in conjunction with a chiral phosphoric acid catalyst. The reaction achieved a variety of interesting 5-arylpyrimidines featuring the union of an axially chiral heterobiaryl and a centrally chiral α-branched amine with generally excellent regio-, diastereo-, and enantioselectivity (up to 82% yield; >19:1 dr; >99% ee). This finding also builds up a new platform for the development of desymmetrization methods via radical-involved atroposelective functionalization at heteroarene of prochiral heterobiaryls.

Photoredox Neutral Decarboxylative Hydroxyalkylations of Heteroarenes with α-Keto Acids

Photoredox neutral decarboxylative hydroxyalkylations of heteroarenes with α-keto acids under mild conditions are described. Stable and readily available α-keto acids were employed as hydroxyalkylating reagents with only CO₂ released as the byproduct. A range of aromatic and aliphatic α-keto acids were successfully converted into hydroxyalkylated products with various heteroarenes. This transformation proceeded through a decarboxylation/Minisci addition/SCS sequence, generating a variety of valuable hydroxyalkylated heteroarenes.

ChemSpaX: exploration of chemical space by automated functionalization of molecular scaffold

Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, ChemSpaX, that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of ChemSpaX by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show that ChemSpaX generated geometries can be used in machine learning applications to accurately predict DFT computed HOMO–LUMO gaps for transition metal complexes. ChemSpaX is open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery.

This work introduces ChemSpaX, an open-source Python-based tool for automated exploration of chemical space of molecular scaffolds with a special focus on transition-metal complexes.

Photons or Electrons? A Critical Comparison of Electrochemistry and Photoredox Catalysis for Organic Synthesis

Redox processes are at the heart of synthetic methods that rely on either electrochemistry or photoredox catalysis, but how do electrochemistry and photoredox catalysis compare? Both approaches provide access to high energy intermediates (e.g., radicals) that enable bond formations not constrained by the rules of ionic or 2 electron (e) mechanisms. Instead, they enable 1e mechanisms capable of bypassing electronic or steric limitations and protecting group requirements, thus enabling synthetic chemists to disconnect molecules in new and different ways. However, while providing access to similar intermediates, electrochemistry and photoredox catalysis differ in several physical chemistry principles. Understanding those differences can be key to designing new transformations and forging new bond disconnections. This review aims to highlight these differences and similarities between electrochemistry and photoredox catalysis by comparing their underlying physical chemistry principles and describing their impact on electrochemical and photochemical methods.

Guiding Target Synthesis with Statistical Modeling Tools: A Case Study in Organocatalysis

Practitioners are generally not willing to explore modern reactions where considerable synthetic effort is required to generate materials and the results are not certain. Organocatalysis exemplifies this, in which a broad set of enantioselective reactions have been successfully developed but further applications to include additional substrates are often not performed. Herein we demonstrate how statistical models can be utilized to accurately distinguish between different catalysts and reactions to guide the selection of efficient synthetic routes to obtain a target molecule.

Recent advances in catalytic enantioselective direct C–H bond functionalization of electron-deficient N-containing heteroarenes

Catalytic enantioselective direct C–H bond functionalization of electron-deficient N-containing heteroarenes represents one of the most straightforward and powerful protocols to construct diverse enantioenriched highly functionalized N-heteroarenes.

Identifying the true origins of selectivity in chiral phosphoric acid catalyzed N-acyl-azetidine desymmetrizations

The first catalytic intermolecular desymmetrization of azetidines was reported by Sun and coworkers in 2015 using a BINOL-derived phosphoric acid catalyst (J. Am. Chem. Soc. 2015, 137, 5895-5898). To uncover the mechanism of the reaction and the origins of the high enantioselectivity, Density Functional Theory (DFT) calculations were performed at the B97D3/6-311+G(2d,2p)/SMD(toluene)//B97D3/6-31G(d,p)/CPCM(toluene) level of theory. Comparison of four possible activation modes confirms that this reaction proceeds through the bifunctional activation of the azetidine nitrogen and the thione tautomer of the 2-mercaptobenzothiazole nucleophile. Upon thorough conformational sampling of the enantiodetermining transition structures (TSs), a free energy difference of 2.0 kcal mol-1 is obtained, accurately reproducing the experimentally measured 88% e.e. at 80 °C. This energy difference is due to both decreased distortion and increased non-covalent interactions in the pro-(S) TS. To uncover the true origins of selectivity, the TSs optimized with the full catalyst were compared to those optimized with a model catalyst through steric maps. It is found that the arrangements displayed by the substrates are controlled by strict primary orbital interaction requirements at the transition complex, and their ability to fit into the catalyst pocket drives the selectivity. A general model of selectivity for phosphoric acid-catalyzed azetidine desymmetrizations is proposed, which is based on the preference of the nucleophile and benzoyl group to occupy empty quadrants of the chiral catalyst pocket.

Construction of α-Amino Azines via Thianthrenation-Enabled Photocatalyzed Hydroarylation of Azine-Substituted Enamides with Arenes

α-Amino azines are widely found in pharmaceuticals and ligands. Herein, we report a practical method for accessing this class of compounds via photocatalyzed hydroarylation of azine-substituted enamides with the in situ-generated aryl thianthrenium salts as the radical precursor. This reaction features a broad substrate scope, good functional group tolerance, and mild conditions and is suitable for the late-stage installation of α-amino azines in complex structures.

Metal-to-Ligand Ratio-Dependent Chemodivergent Asymmetric Synthesis

Chemodivergent asymmetric synthesis was achieved by tuning the metal-to-ligand ratio in an organometallic catalytic system. Using N-(aroyloxy)phthalimide as the precursor of either an oxygen-centered aroyloxy radical or a nitrogen-centered phthalimidyl radical, enantioselective oxocyanation or aminocyanation of alkenes was achieved separately through a dual photoredox and copper catalysis. The metal-to-ligand ratio can exert chemoselective control while retaining the high enantiopurity of divergent products. Both reactions proceed efficiently with catalyst loading as low as 0.2 mol % and can be performed on a gram scale without loss of chemoselectivity or enantioselectivity. Chemodivergent asymmetric 1,5-aminocyanation or 1,5-oxocyanation of vinylcyclopropane can also be realized by this protocol. Mechanistic investigations involving electron paramagnetic resonance (EPR) experiments were performed to shed light on the stereochemical and chemodivergent results.

Molecular Dynamics and Machine Learning in Catalysts

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.

Predicting Reaction Yields via Supervised Learning

Numerous disciplines, such as image recognition and language translation, have been revolutionized by using machine learning (ML) to leverage big data. In organic synthesis, providing accurate chemical reactivity predictions with supervised ML could assist chemists with reaction prediction, optimization, and mechanistic interrogation.To apply supervised ML to chemical reactions, one needs to define the object of prediction (e.g., yield, enantioselectivity, solubility, or a recommendation) and represent reactions with descriptive data. Our group's effort has focused on representing chemical reactions using DFT-derived physical features of the reacting molecules and conditions, which serve as features for building supervised ML models.In this Account, we present a review and perspective on three studies conducted by our group where ML models have been employed to predict reaction yield. First, we focus on a small reaction data set where 16 phosphine ligands were evaluated in a single Ni-catalyzed Suzuki-Miyaura cross-coupling reaction, and the reaction yield was modeled with linear regression. In this setting, where the regression complexity is strongly limited by the amount of available data, we emphasize the importance of identifying single features that are directly relevant to reactivity. Next, we focus on models trained on two larger data sets obtained with high-throughput experimentation (HTE). With hundreds to thousands of reactions available, more complex models can be explored, for example, models that algorithmically perform feature selection from a broad set of candidate features. We examine how a variety of ML algorithms model these data sets and how well these models generalize to out-of-sample substrates. Specifically, we compare the ML models that use DFT-based featurization to a baseline model that is obtained with features that carry no physical information, that is, random features, and to a naive non-ML model that averages yields of reactions that share the same conditions and substrate combinations. We find that for only one of the two data sets, DFT-based featurization leads to a significant, although moderate, out-of-sample prediction improvement. The source of this improvement was further isolated to specific features which allowed us to formulate a testable mechanistic hypothesis that was validated experimentally. Finally, we offer remarks on supervised ML model building on HTE data sets focusing on algorithmic improvements in model training.Statistical methods in chemistry have a rich history, but only recently has ML gained widespread attention in reaction development. As the untapped potential of ML is explored, novel tools are likely to arise from future research. Our studies suggest that supervised ML can lead to improved predictions of reaction yield over simpler modeling methods and facilitate mechanistic understanding of reaction dynamics. However, further research and development is required to establish ML as an indispensable tool in reactivity modeling.

Hydrogen Atom Transfer-Driven Enantioselective Minisci Reaction of Amides

Minisci-type reactions constitute one of the most powerful methods for building up complexity around basic heteroarenes. The most desirable variants involve formal oxidative coupling of a C-H bond on each partner, leading back to the simplest possible starting materials. We herein disclose a method that enables such a coupling of linear amides and heteroarenes with full control of enantioselectivity at the newly formed stereocenter as well as site selectivity on both the heteroarene and the amide. This is achieved by the use of a chiral phosphoric acid catalyst in conjunction with diacetyl as a combined hydrogen atom transfer reagent and oxidant. Diacetyl is directly photoexcitable, and thus, no extraneous photocatalyst is required: an added feature that contributes to the simplicity and practicality of the protocol.

Organic reactivity from mechanism to machine learning

As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.

Electro-Descriptors for the Performance Prediction of Electro-Organic Synthesis

Electrochemical organic synthesis has attracted increasing attentions as a sustainable and versatile synthetic platform. Quantitative assessment of the electro-organic reactions, including reaction thermodynamics, electro-kinetics, and coupled chemical processes, can lead to effective analytical tool to guide their future design. Herein, we demonstrate that electrochemical parameters such as onset potential, Tafel slope, and effective voltage can be utilized as electro-descriptors for the evaluation of reaction conditions and prediction of reactivities (yields). An "electro-descriptor-diagram" is generated, where reactive and non-reactive conditions/substances show distinct boundary. Successful predictions of reaction outcomes have been demonstrated using electro-descriptor diagram, or from machine learning algorithms with experimentally-derived electro-descriptors. This method represents a promising tool for data-acquisition, reaction prediction, mechanistic investigation, and high-throughput screening for general organic electro-synthesis.

Radical-Based Synthesis and Modification of Amino Acids

Amino acids (AAs) are key structural motifs with widespread applications in organic synthesis, biochemistry, and material sciences. Recently, with the development of milder and more versatile radical-based procedures, the use of strategies relying on radical chemistry for the synthesis and modification of AAs has gained increased attention, as they allow rapid access to libraries of novel unnatural AAs containing a wide range of structural motifs. In this Minireview, we provide a broad overview of the advancements made in this field during the last decade, focusing on methods for the de novo synthesis of α-, β-, and γ-AAs, as well as for the selective derivatisation of canonical and non-canonical α-AAs.

Artificial intelligence and automation in computer aided synthesis planning

In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.

A Computational and Experimental Investigation of the Origin of Selectivity in the Chiral Phosphoric Acid Catalyzed Enantioselective Minisci Reaction

The Minisci reaction is one of the most valuable methods for directly functionalizing basic heteroarenes to form carbon–carbon bonds. Use of prochiral, heteroatom-substituted radicals results in stereocenters being formed adjacent to the heteroaromatic system, generating motifs which are valuable in medicinal chemistry and chiral ligand design. Recently a highly enantioselective and regioselective protocol for the Minisci reaction was developed, using chiral phosphoric acid catalysis. However, the precise mechanism by which this process operated and the origin of selectivity remained unclear, making it challenging to develop the reaction more generally. Herein we report further experimental mechanistic studies which feed into detailed DFT calculations that probe the precise nature of the stereochemistry-determining step. Computational and experimental evidence together support Curtin–Hammett control in this reaction, with initial radical addition being quick and reversible, and enantioselectivity being achieved in the subsequent slower, irreversible deprotonation. A detailed survey via DFT calculations assessed a number of different possibilities for selectivity-determining deprotonation of the radical cation intermediate. Computations point to a clear preference for an initially unexpected mode of internal deprotonation enacted by the amide group, which is a crucial structural feature of the radical precursor, with the assistance of the associated chiral phosphate. This unconventional stereodetermining step underpins the high enantioselectivities and regioselectivities observed. The mechanistic model was further validated by applying it to a test set of substrates possessing varied structural features.

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.