Chemistry informer libraries: a chemoinformatics enabled approach to evaluate and advance synthetic methods

Persistent organonickel complexes as general platforms for Csp2-Csp3 coupling reactions

The importance of constructing Csp2-Csp3 bonds has motivated the development of electrochemical, photochemical and thermal activation methods to reductively couple abundant aryl and alkyl electrophiles. However, these methodologies are limited to couplings of very specific substrate classes and require specialized sets of catalysts and reaction set-ups. Here we show a consolidation of these myriad strategies into a single set of conditions that enable reliable alkyl-aryl couplings, including those that were previously unknown. These reactions rely on the discovery of unusually persistent organonickel complexes that serve as stoichiometric platforms for C(sp2)-C(sp3) coupling. Aryl, heteroaryl or vinyl complexes of Ni can be inexpensively prepared on a multigram scale by mild electroreduction from the corresponding C(sp2) electrophile. Organonickel complexes can be isolated and stored or telescoped directly to reliably diversify drug-like molecules. Finally, the procedure was miniaturized to micromole scales by integrating soluble battery chemistries as redox initiators, enabling a high-throughput exploration of substrate diversity.

Synthesis and Suzuki-Miyaura Cross-Coupling of Alkyl Amine-Boranes. A Boryl Radical-Enabled Strategy

Alkyl organoborons are powerful materials for the construction of C(sp3)-C(sp2) bonds, predominantly via Suzuki-Miyaura cross-coupling. These species are generally assembled using 2-electron processes that harness the ability of boron reagents to act as both electrophiles and nucleophiles. Herein, we demonstrate an alternative borylation strategy based on the reactivity of amine-ligated boryl radicals. This process features the use of a carboxylic acid containing amine-ligated borane that acts as boryl radical precursor for photoredox oxidation and decarboxylation. The resulting amine-ligated boryl radical undergoes facile addition to styrenes and imines through radical-polar crossover manifolds. This delivers a new class of sp3-organoborons that are stable solids and do not undergo protodeboronation. These novel materials include unprotected α-amino derivatives that are generally unstable. Crucially, these aliphatic organoboron species can be directly engaged in Suzuki-Miyaura cross-couplings with structurally complex aryl halides. Preliminary studies suggest that they enable slow-release of the corresponding and often difficult to handle alkyl boronic acids.

Data-Driven Insights into the Transition-Metal-Catalyzed Asymmetric Hydrogenation of Olefins

The transition-metal-catalyzed asymmetric hydrogenation of olefins is one of the key transformations with great utility in various industrial applications. The field has been dominated by the use of noble metal catalysts, such as iridium and rhodium. The reactions with the earth-abundant cobalt metal have increased only in recent years. In this work, we analyze the large amount of literature data available on iridium- and rhodium-catalyzed asymmetric hydrogenation. The limited data on reactions using Co catalysts are then examined in the context of Ir and Rh to obtain a better understanding of the reactivity pattern. A detailed data-driven study of the types of olefins, ligands, and reaction conditions such as solvent, temperature, and pressure is carried out. Our analysis provides an understanding of the literature trends and demonstrates that only a few olefin-ligand combinations or reaction conditions are frequently used. The knowledge of this bias in the literature data toward a certain group of substrates or reaction conditions can be useful for practitioners to design new reaction data sets that are suitable to obtain meaningful predictions from machine-learning models.

A General Three-Component Alkyl Petasis Boron-Mannich Reaction

Aryl amines are one of the most common moieties in biologically active molecules, and approximately 37% of drug candidates contain aromatic amines. Recent advancements in medicinal chemistry, coined "escaping from flatland", have led to a greater focus on accessing highly functionalized C (sp3)-rich amines to improve the physicochemical and pharmacokinetic properties of compounds. This article presents a modular and operationally straightforward three-component alkyl Petasis boron-Mannich (APBM) reaction that utilizes ubiquitous starting materials, including amines, aldehydes, and alkyl boronates. By adaptation of this transformation to high-throughput experimentation (HTE), it offers rapid access to an array of diverse C(sp3)-rich complex amines, amenable for rapid identification of drug candidates.

Homogeneous Organic Reductant Based on 4,4'-tBu2-2,2'-Bipyridine for Cross-Electrophile Coupling

The synthesis of a new homogeneous reductant based on 4,4'-tBu2-2,2'-bipyridine, tBu-OED4, is reported. tBu-OED4 was prepared on a multigram scale in two steps from inexpensive and commercially available starting materials, with no chromatography required for purification. tBu-OED4 has a reduction potential of -1.33 V (vs Ferrocenium/Ferrocene) and is soluble in a range of common organic solvents. We demonstrate that tBu-OED4 can facilitate Ni/Co dual-catalyzed C(sp2)-C(sp3) cross-electrophile coupling reactions and is highly functional group tolerant. tBu-OED4 is expected to be a valuable addition to the set of homogeneous reductants available for organic transformations.

Effect of 6,6'-Substituents on Bipyridine-Ligated Ni Catalysts for Cross-Electrophile Coupling

A family of 4,4'-tBu2-2,2'-bipyridine (tBubpy) ligands with substituents in either the 6-position, 4,4'-tBu2-6-Me-bpy (tBubpyMe), or 6 and 6'-positions, 4,4'-tBu2-6,6'-R2-bpy (tBubpyR2; R = Me, iPr, sBu, Ph, or Mes), was synthesized. These ligands were used to prepare Ni complexes in the 0, I, and II oxidation states. We observed that the substituents in the 6 and 6'-positions of the tBubpy ligand impact the properties of the Ni complexes. For example, bulkier substituents in the 6,6'-positions of tBubpy better stabilized (tBubpyR2)NiICl species and resulted in cleaner reduction from (tBubpyR2)NiIICl2. However, bulkier substituents hindered or prevented coordination of tBubpyR2 ligands to Ni0(cod)2. In addition, by using complexes of the type (tBubpyMe)NiCl2 and (tBubpyR2)NiCl2 as precatalysts for different XEC reactions, we demonstrated that the 6 or 6,6' substituents lead to major differences in catalytic performance. Specifically, while (tBubpyMe)NiIICl2 is one of the most active catalysts reported to date for XEC and can facilitate XEC reactions at room temperature, lower turnover frequencies were observed for catalysts containing tBubpyR2 ligands. A detailed study on the catalytic intermediates (tBubpy)Ni(Ar)I and (tBubpyMe2)Ni(Ar)I revealed several factors that likely contributed to the differences in catalytic activity. For example, whereas complexes of the type (tBubpy)Ni(Ar)I are low spin and relatively stable, complexes of the type (tBubpyMe2)Ni(Ar)I are high-spin and less stable. Further, (tBubpyMe2)Ni(Ar)I captures primary and benzylic alkyl radicals more slowly than (tBubpy)Ni(Ar)I, consistent with the lower activity of the former in catalysis. Our findings will assist in the design of tailor-made ligands for Ni-catalyzed transformations.

Innovative synthesis of drug-like molecules using tetrazole as core building blocks

Tetrazole is widely utilized as a bioisostere for carboxylic acid in the field of medicinal chemistry and drug development, enhancing the drug-like characteristics of various molecules. Typically, tetrazoles are introduced from their nitrile precursors through late-stage functionalization. In this work, we propose a novel strategy involving the use of diversely protected, unprecedented tetrazole aldehydes as building blocks. This approach facilitates the incorporation of the tetrazole group into multicomponent reactions or other chemistries, aiding in the creation of a variety of complex, drug-like molecules. These innovative tetrazole building blocks are efficiently and directly synthesized using a Passerini three-component reaction (PT-3CR), employing cost-effective and readily available materials. We further showcase the versatility of these new tetrazole building blocks by integrating the tetrazole moiety into various multicomponent reactions (MCRs), which are already significantly employed in drug discovery. This technique represents a unique and complementary method to existing tetrazole synthesis processes. It aims to meet the growing demand for tetrazole-based compound libraries and novel scaffolds, which are challenging to synthesize through other methods.

Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality

With over 10,000 new reaction protocols arising every year, only a handful of these procedures transition from academia to application. A major reason for this gap stems from the lack of comprehensive knowledge about a reaction's scope, i.e., to which substrates the protocol can or cannot be applied. Even though chemists invest substantial effort to assess the scope of new protocols, the resulting scope tables involve significant biases, reducing their expressiveness. Herein we report a standardized substrate selection strategy designed to mitigate these biases and evaluate the applicability, as well as the limits, of any chemical reaction. Unsupervised learning is utilized to map the chemical space of industrially relevant molecules. Subsequently, potential substrate candidates are projected onto this universal map, enabling the selection of a structurally diverse set of substrates with optimal relevance and coverage. By testing our methodology on different chemical reactions, we were able to demonstrate its effectiveness in finding general reactivity trends by using a few highly representative examples. The developed methodology empowers chemists to showcase the unbiased applicability of novel methodologies, facilitating their practical applications. We hope that this work will trigger interdisciplinary discussions about biases in synthetic chemistry, leading to improved data quality.

Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration

Positron emission tomography is a widely used imaging platform for studying physiological processes. Despite the proliferation of modern synthetic methodologies for radiolabeling, the optimization of these reactions still primarily relies on inefficient one-factor-at-a-time approaches. High-throughput experimentation (HTE) has proven to be a powerful approach for optimizing reactions in many areas of chemical synthesis. However, to date, HTE has rarely been applied to radiochemistry. This is largely because of the short lifetime of common radioisotopes, which presents major challenges for efficient parallel reaction setup and analysis using standard equipment and workflows. Herein, we demonstrate an effective HTE workflow and apply it to the optimization of copper-mediated radiofluorination of pharmaceutically relevant boronate ester substrates. The workflow utilizes commercial equipment and allows for rapid analysis of reactions for optimizing reactions, exploring chemical space using pharmaceutically relevant aryl boronates for radiofluorinations, and constructing large radiochemistry data sets.

Bridging the information gap in organic chemical reactions

The varying quality of scientific reports is a well-recognized problem and often results from a lack of standardization and transparency in scientific publications. This situation ultimately leads to prominent complications such as reproducibility issues and the slow uptake of newly developed synthetic methods for pharmaceutical and agrochemical applications. In recent years, various impactful approaches have been advocated to bridge information gaps and to improve the quality of experimental protocols in synthetic organic publications. Here we provide a critical overview of these strategies and present the reader with a versatile set of tools to augment their standard procedures. We formulate eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards. We are aware that this is a substantial effort, but we are convinced that the resulting improved data situation will greatly benefit the progress of chemistry.

Probing the chemical 'reactome' with high-throughput experimentation data

High-throughput experimentation (HTE) has the potential to improve our understanding of organic chemistry by systematically interrogating reactivity across diverse chemical spaces. Notable bottlenecks include few publicly available large-scale datasets and the need for facile interpretation of these data's hidden chemical insights. Here we report the development of a high-throughput experimentation analyser, a robust and statistically rigorous framework, which is applicable to any HTE dataset regardless of size, scope or target reaction outcome, which yields interpretable correlations between starting material(s), reagents and outcomes. We improve the HTE data landscape with the disclosure of 39,000+ previously proprietary HTE reactions that cover a breadth of chemistry, including cross-coupling reactions and chiral salt resolutions. The high-throughput experimentation analyser was validated on cross-coupling and hydrogenation datasets, showcasing the elucidation of statistically significant hidden relationships between reaction components and outcomes, as well as highlighting areas of dataset bias and the specific reaction spaces that necessitate further investigation.

Ligated Pd-Catalyzed Aminations of Aryl/Heteroaryl Halides with Aliphatic Amines under Sustainable Aqueous Micellar Conditions

Sustainable technology for constructing Pd-catalyzed C-N bonds involving aliphatic amines is reported. A catalytic system that relies on low levels of recyclable precious metal, a known and commercially available ligand, and a recyclable aqueous medium are combined, leading to a newly developed procedure. This new technology can be used in ocean water with equal effectiveness. Applications involving highly challenging reaction partners constituting late-stage functionalization are documented, as is a short but efficient synthesis of the drug naftopidil. Comparisons with existing aminations highlight the many advances being offered.

Nanoparticles as Heterogeneous Catalysts for ppm Pd-Catalyzed Aminations in Water

A general protocol employing heterogeneous catalysis has been developed that enables ppm of Pd-catalyzed C-N cross-coupling reactions under aqueous micellar catalysis. A new nanoparticle catalyst containing specifically ligated Pd, in combination with nanoreactors composed of the designer surfactant Savie, a biodegradable amphiphile, catalyzes C-N bond formations in recyclable water. A variety of coupling partners, ranging from highly functionalized pharmaceutically relevant APIs to educts from the Merck Informer Library, readily participate under these environmentally responsible, sustainable reaction conditions. Other key features associated with this report include the low levels of residual Pd found in the products, the recyclability of the aqueous reaction medium, the use of ocean water as an alternative source of reaction medium, options for the use of pseudohalides as alternative reaction partners, and associated low E factors. In addition, an unprecedented 5-step, one-pot sequence is presented, featuring several of the most widely used transformations in the pharmaceutical industry, suggesting potential industrial applications.

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4-5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F-score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization.

Nickel-Catalyzed Reductive Arylation of Redox Active Esters for the Synthesis of α-Aryl Nitriles: Investigation of a Chlorosilane Additive

A nickel-catalyzed reductive cross-coupling of redox active N-hydroxyphthalimide (NHP) esters and iodoarenes for the synthesis of α-aryl nitriles is described. The NHP ester substrate is derived from cyanoacetic acid, which allows for a modular synthesis of substituted α-aryl nitriles, an important scaffold in the pharmaceutical sciences. The reaction exhibits a broad scope, and many functional groups are compatible under the reaction conditions, including complex highly functionalized medicinal agents. Mechanistic studies reveal that reduction and decarboxylation of the NHP ester to the reactive radical intermediate are accomplished by a combination of a chlorosilane additive and Zn dust. We demonstrate that stoichiometric chlorosilane is essential for product formation and that chlorosilane plays a role beyond activation of the metal reductant.

When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges

Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.

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.

A Photochemical Strategy for the Conversion of Nitroarenes into Rigidified Pyrrolidine Analogues

Bicyclic amines are important motifs for the preparation of bioactive materials. These species have well-defined exit vectors that enable accurate disposition of substituents toward specific areas of chemical space. Of all possible skeletons, the 2-azabicyclo[3.2.0]heptane framework is virtually absent from MedChem libraries due to a paucity of synthetic methods for its preparation. Here, we report a modular synthetic strategy that utilizes nitroarenes as flat and easy-to-functionalize feedstocks for the assembly of these sp3-rich materials. Mechanistically, this approach exploits two concomitant photochemical processes that sequentially ring-expand the nitroarene into an azepine and then fold it into a rigid bicycle pyrroline by means of singlet nitrene-mediated nitrogen insertion and excited-state-4π electrocyclization. A following hydrogenolysis provides, with full diastereocontrol, the desired bicyclic amine derivatives whereby the aromatic substitution pattern has been translated into the one of the three-dimensional heterocycle. These molecules can be considered rigid pyrrolidine analogues with a well-defined orientation of their substituents. Furthermore, unsupervised clustering of an expansive virtual database of saturated N-heterocycles revealed these derivatives as effective isosteres of rigidified piperidines. Overall, this platform enables the conversion of nitroarene feedstocks into complex sp3-rich heterocycles of potential interest to drug development.

Challenging cross couplings, in water, aided by in situ iodination of (hetero)aromatic bromides

Palladium-catalyzed reactions that involve functionalized substrates are oftentimes problematic. Those involving aryl or heteroaryl bromides that are either resistant to, or inefficient in such couplings present challenges that are difficult to overcome and may require development of an entirely new route, or worse, no opportunity to install the desired group using a standard coupling strategy. In this report, we describe a solution that allows for the in situ conversion of such bromo educts to transient iodide derivatives that can be made and used under environmentally responsible conditions, for subsequent reactions to highly functionalized, complex targets.

Primary vs. secondary alkylpyridinium salts: a comparison under electrochemical and chemical reduction conditions

This report details a systematic comparison of the scope of aryl bromides in nickel-catalyzed, reductive cross-electrophile couplings of primary vs. secondary alkylpyridinium salts using both electrochemical and chemical reductants. Facilitated by the use of high-throughput experimentation (HTE) techniques, 37 aryl bromides, including 13 complex, drug-like examples, were investigated. By using primary and secondary substrates differing only by one methylene, we observed that the trends in ArBr scope are similar between the primary and secondary alkylpyridinium salts, although distinctions were observed in isolated cases. In addition, the electrochemical conditions compared favorably to those using chemical reductants, especially among the more complex, drug-like aryl halides.

High-throughput synthesis provides data for predicting molecular properties and reaction success

The generation of attractive scaffolds for drug discovery efforts requires the expeditious synthesis of diverse analogues from readily available building blocks. This endeavor necessitates a trade-off between diversity and ease of access and is further complicated by uncertainty about the synthesizability and pharmacokinetic properties of the resulting compounds. Here, we document a platform that leverages photocatalytic N-heterocycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on 1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereochemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and properties. This study exemplifies how organic chemists can exploit state-of-the-art technologies to markedly increase throughput and confidence in the preparation of drug-like molecules.

Accessing three-dimensional molecular diversity through benzylic C-H cross-coupling

Pharmaceutical and agrochemical discovery efforts rely on robust methods for chemical synthesis that rapidly access diverse molecules1,2. Cross-coupling reactions are the most widely used synthetic methods3, but these methods typically form bonds to C(sp2)-hybridized carbon atoms (e.g., amide coupling, biaryl coupling) and lead to a prevalence of "flat" molecular structures with suboptimal physicochemical and topological properties4. Benzylic C(sp3)-H cross-coupling methods offer an appealing strategy to address this limitation by directly forming bonds to C(sp3)-hybridized carbon atoms, and emerging methods exhibit synthetic versatility that rivals conventional cross-coupling methods to access products with drug-like properties. Here, we use a virtual library of >350,000 benzylic ethers and ureas derived from benzylic C-H cross-coupling to test the widely held view that coupling at C(sp3)-hybridized carbon atoms affords products with improved three-dimensionality. The results show that the conformational rigidity of the benzylic scaffold strongly influences the product dimensionality. Products derived from flexible scaffolds often exhibit little or no improvement in three-dimensionality, unless they adopt higher energy conformations. This outcome introduces an important consideration when designing routes to topologically diverse molecular libraries. The concepts elaborated herein are validated experimentally through an informatics-guided synthesis of selected targets and the use of high-throughput experimentation to prepare a library of three-dimensional products that are broadly distributed across drug-like chemical space.

Palladium-Catalyzed Trans-Selective Synthesis of Spirocyclic Cyclobutanes Using α,α-Dialkylcrotyl- and Allylhydrazones

Herein, we demonstrate the use of E/Z mixtures of α,α-disubstituted crotylhydrazones to obtain spirocyclic vinylcyclobutanes in a diastereoselective fashion. We show 24 examples of a 1,1-insertion/4-exo-trig tandem process to produce these motifs. Additionally, spirocyclic alkylidene cyclobutanes can be obtained by using α,α-disubstituted allylated hydrazones (11 examples). In this study, we show that the aryl migrating group has a dramatic impact on the course of the reaction. Specifically, allylic C-H insertion products can be obtained in good yields using bromoenones as reaction partners. When Pd(0) is used with no aryl or alkenyl bromide, an intramolecular cyclopropanation reaction takes place to afford [2.1.0]-bicycles.

Translating Planar Heterocycles into Three-Dimensional Analogs by Photoinduced Hydrocarboxylation

The rapid preparation of complex three-dimensional (3D) heterocyclic scaffolds is a key challenge in modern medicinal chemistry. Despite the increased probability of clinical success for small molecule therapeutic candidates with increased 3D complexity, new drug targets remain dominated by flat molecules due to the abundance of coupling reactions available for their construction. In principle, heteroarene hydrofunctionalization reactions offer an opportunity to transform readily accessible planar molecules into more three-dimensionally complex analogs through the introduction of a single molecular vector. Unfortunately, dearomative hydrofunctionalization reactions remain limited. Herein, we report a new strategy to enable the dearomative hydrocarboxylation of indoles and related heterocycles. This reaction represents a rare example of a heteroarene hydrofunctionalization that meets the numerous requirements for broad implementation in drug discovery. The transformation is highly chemoselective, broad in scope, operationally simple, and readily amenable to high-throughput experimentation (HTE). Accordingly, this process will allow existing libraries of heteroaromatic compounds to be translated into diverse 3D analogs and enable exploration of new classes of medicinally relevant molecules.

Nickel-Catalyzed Electroreductive Coupling of Alkylpyridinium Salts and Aryl Halides

An electrochemical, nickel-catalyzed reductive coupling of alkylpyridinium salts and aryl halides is reported. High-throughput experimentation (HTE) was employed for rapid reaction optimization and evaluation of a broad scope of pharmaceutically relevant structurally diverse aryl halides, including complex drug-like substrates. In addition, the transformation is compatible with both primary and secondary alkylpyridinium salts with distinct conditions. Mechanistic insights were critical to enhance the efficiency of coupling using secondary alkylpyridinium salts. Systematic comparisons of the electrochemical and non-electrochemical methods revealed the complementary scope and efficiency of the two approaches.

Recent Progress on One-Pot Multisubstrate Screening

Traditionally, new synthetic reactions have been developed using a model substrate to screen reaction conditions before testing the optimized conditions with a range of more complex substrates. In 1998, Gao and Kagan pooled multiple substrates in one pot to study the generality of an enantioselective method. Although such one-pot multisubstrate screenings may be powerful, few applications have appeared in the literature. With the advancement of various chromatography techniques, it may be time to revisit this underutilized platform. This review article discusses the applications of one-pot multisubstrate screenings as a method for developing new synthetic methods.

A preparative small-molecule mimic of liver CYP450 enzymes in the aliphatic C-H oxidation of carbocyclic N-heterocycles

An emerging trend in small-molecule pharmaceuticals, generally composed of nitrogen heterocycles (N-heterocycles), is the incorporation of aliphatic fragments. Derivatization of the aliphatic fragments to improve drug properties or identify metabolites often requires lengthy de novo syntheses. Cytochrome P450 (CYP450) enzymes are capable of direct site- and chemo-selective oxidation of a broad range of substrates but are not preparative. A chemoinformatic analysis underscored limited structural diversity of N-heterocyclic substrates oxidized using chemical methods relative to pharmaceutical chemical space. Here, we describe a preparative chemical method for direct aliphatic oxidation that tolerates a wide range of nitrogen functionality (chemoselective) and matches the site of oxidation (site-selective) of liver CYP450 enzymes. Commercial small-molecule catalyst Mn(CF3-PDP) selectively effects direct methylene oxidation in compounds bearing 25 distinct heterocycles including 14 out of 27 of the most frequent N-heterocycles found in U.S. Food and Drug Administration (FDA)-approved drugs. Mn(CF3-PDP) oxidations of carbocyclic bioisostere drug candidates (for example, HCV NS5B and COX-2 inhibitors including valdecoxib and celecoxib derivatives) and precursors of antipsychotic drugs blonanserin, buspirone, and tiospirone and the fungicide penconazole are demonstrated to match the major site of aliphatic metabolism obtained with liver microsomes. Oxidations are demonstrated at low Mn(CF3-PDP) loadings (2.5 to 5 mol%) on gram scales of substrate to furnish preparative amounts of oxidized products. A chemoinformatic analysis supports that Mn(CF3-PDP) significantly expands the pharmaceutical chemical space accessible to small-molecule C-H oxidation catalysis.

Rapid planning and analysis of high-throughput experiment arrays for reaction discovery

High-throughput experimentation (HTE) is an increasingly important tool in reaction discovery. While the hardware for running HTE in the chemical laboratory has evolved significantly in recent years, there remains a need for software solutions to navigate data-rich experiments. Here we have developed phactor™, a software that facilitates the performance and analysis of HTE in a chemical laboratory. phactor™ allows experimentalists to rapidly design arrays of chemical reactions or direct-to-biology experiments in 24, 96, 384, or 1,536 wellplates. Users can access online reagent data, such as a chemical inventory, to virtually populate wells with experiments and produce instructions to perform the reaction array manually, or with the assistance of a liquid handling robot. After completion of the reaction array, analytical results can be uploaded for facile evaluation, and to guide the next series of experiments. All chemical data, metadata, and results are stored in machine-readable formats that are readily translatable to various software. We also demonstrate the use of phactor™ in the discovery of several chemistries, including the identification of a low micromolar inhibitor of the SARS-CoV-2 main protease. Furthermore, phactor™ has been made available for free academic use in 24- and 96-well formats via an online interface.

Late-Stage C-H Functionalization of Azines

Azines, such as pyridines, quinolines, pyrimidines, and pyridazines, are widespread components of pharmaceuticals. Their occurrence derives from a suite of physiochemical properties that match key criteria in drug design and is tunable by varying their substituents. Developments in synthetic chemistry, therefore, directly impact these efforts, and methods that can install various groups from azine C-H bonds are particularly valuable. Furthermore, there is a growing interest in late-stage functionalization (LSF) reactions that focus on advanced candidate compounds that are often complex structures with multiple heterocycles, functional groups, and reactive sites. Because of factors such as their electron-deficient nature and the effects of the Lewis basic N atom, azine C-H functionalization reactions are often distinct from their arene counterparts, and the application of these reactions in LSF contexts is difficult. However, there have been many significant advances in azine LSF reactions, and this review will describe this progress, much of which has occurred over the past decade. It is possible to categorize these reactions as radical addition processes, metal-catalyzed C-H activation reactions, and transformations occurring via dearomatized intermediates. Substantial variation in reaction design within each category indicates both the rich reactivity of these heterocycles and the creativity of the approaches involved.

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge

Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.

Enabling Deoxygenative C(sp2)-C(sp3) Cross-Coupling for Parallel Medicinal Chemistry

Herein we report the development of an automated deoxygenative C(sp2)-C(sp3) coupling of aryl bromide with alcohols to enable parallel medicinal chemistry. Alcohols are among the most diverse and abundant building blocks, but their usage as alkyl precursors has been limited. Although metallaphotoredox deoxygenative coupling is becoming a promising strategy to form C(sp2)-C(sp3) bond, the reaction setup limits its widespread application in library synthesis. To achieve high throughput and consistency, an automated workflow involving solid-dosing and liquid-handling robots has been developed. We have successfully demonstrated this high-throughput protocol is robust and consistent across three automation platforms. Furthermore, guided by cheminformatic analysis, we examined alcohols with comprehensive chemical space coverage and established a meaningful scope for medicinal chemistry applications. By accessing the rich diversity of alcohols, this automated protocol has the potential to substantially increase the impact of C(sp2)-C(sp3) cross-coupling in drug discovery.

Iron-mediated modular decarboxylative cross-nucleophile coupling

Carboxylic acids are valuable building blocks for pharmaceutical discovery because of their chemical stability, commercial availability, and structural diversity. Decarboxylative coupling reactions enable versatile functionalization of these feedstock chemicals, but many of the most general methods require prefunctionalization of carboxylic acids with redox-active moieties. These internal oxidants can be costly, their installation impedes rapid library synthesis, and their use results in environmentally problematic organic byproducts. We report herein a method for the direct decarboxylative cross-coupling of native carboxylic acids with nucleophilic coupling partners mediated by inexpensive, terrestrially abundant, and nontoxic Fe(III) salts. This method involves an initial photochemical decarboxylation followed by radical-polar crossover, which enables the construction of diverse carbon-carbon, carbon-oxygen, and carbon-nitrogen bonds with remarkable generality.

Room-Temperature Cu-Catalyzed Amination of Aryl Bromides Enabled by DFT-Guided Ligand Design

Ullmann-type C-N coupling reactions represent an important alternative to well-established Pd-catalyzed approaches due to the differing reactivity and the lower cost of Cu. While the design of anionic Cu ligands, particularly those by Ma, has enabled the coupling of various classes of aryl halides and alkyl amines, most methods require conditions that can limit their utility on complex substrates. Herein, we disclose the development of anionic N1,N2-diarylbenzene-1,2-diamine ligands that promote the Cu-catalyzed amination of aryl bromides under mild conditions. Guided by DFT calculations, these ligands were designed to (1) increase the electron density on Cu, thereby increasing the rate of oxidative addition of aryl bromides, and (2) stabilize the active anionic CuI complex via a π-interaction. Under optimized conditions, structurally diverse aryl and heteroaryl bromides and a broad range of alkyl amine nucleophiles, including pharmaceuticals bearing multiple functional groups, were efficiently coupled at room temperature. Combined computational and experimental studies support a mechanism of C-N bond formation that follows a catalytic cycle akin to the well-explored Pd-catalyzed variants. Modification of the ligand structure to include a naphthyl residue resulted in a lower energy barrier to oxidative addition, providing a 30-fold rate increase relative to what is seen with other ligands. Collectively, these results establish a new class of anionic ligands for Cu-catalyzed C-N couplings, which we anticipate may be extended to other Cu-catalyzed C-heteroatom and C-C bond-forming reactions.

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.

Overcoming Limitations in Decarboxylative Arylation via Ag-Ni Electrocatalysis

A useful protocol for achieving decarboxylative cross-coupling (DCC) of redox-active esters (RAE, isolated or generated in situ) and halo(hetero)arenes is reported. This pragmatically focused study employs a unique Ag-Ni electrocatalytic platform to overcome numerous limitations that have plagued this strategically powerful transformation. In its optimized form, coupling partners can be combined in a surprisingly simple way: open to the air, using technical-grade solvents, an inexpensive ligand and Ni source, and substoichiometric AgNO3, proceeding at room temperature with a simple commercial potentiostat. Most importantly, all of the results are placed into context by benchmarking with state-of-the-art methods. Applications are presented that simplify synthesis and rapidly enable access to challenging chemical space. Finally, adaptation to multiple scale regimes, ranging from parallel milligram-based synthesis to decagram recirculating flow is presented.

A Transfer Learning Approach for Reaction Discovery in Small Data Situations Using Generative Model

Sustainable practices in chemical sciences can be better realized by adopting interdisciplinary approaches that combine the advantages of machine learning (ML) on the initially acquired small data in reaction discovery. Developing new reactions generally remains heuristic and even time and resource intensive. For instance, synthesis of fluorine-containing compounds, which constitute ∼20% of the marketed drugs, relies on deoxyfluorination of abundantly available alcohols. Herein, we demonstrate the use of a recurrent neural network-based deep generative model built on a library of just 37 alcohols for effective learning and exploration of the chemical space. The proof-of-concept ML model is able to generate good quality, synthetically accessible, higher-yielding novel alcohol molecules. This protocol would have superior utility for deployment into a practical reaction discovery pipeline.

•

Dual pronged transfer learning, both to generate and predict yields of new molecules

•

Demonstrated the utility for an important family of deoxyfluorination of alcohols

•

Applicable for practically more likely situations with relatively smaller data

•

Extendable to other reaction manifolds to facilitate expedited reaction discovery

A Unified Approach to Decarboxylative Halogenation of (Hetero)aryl Carboxylic Acids

Aryl halides are a fundamental motif in synthetic chemistry, playing a critical role in metal-mediated cross-coupling reactions and serving as important scaffolds in drug discovery. Although thermal decarboxylative functionalization of aryl carboxylic acids has been extensively explored, the scope of existing halodecarboxylation methods remains limited, and there currently exists no unified strategy that provides access to any type of aryl halide from an aryl carboxylic acid precursor. Herein, we report a general catalytic method for direct decarboxylative halogenation of (hetero)aryl carboxylic acids via ligand-to-metal charge transfer. This strategy accommodates an exceptionally broad scope of substrates. We leverage an aryl radical intermediate toward divergent functionalization pathways: (1) atom transfer to access bromo- or iodo(hetero)arenes or (2) radical capture by copper and subsequent reductive elimination to generate chloro- or fluoro(hetero)arenes. The proposed ligand-to-metal charge transfer mechanism is supported through an array of spectroscopic studies.

Merging Directed C-H Activations with High-Throughput Experimentation: Development of Iridium-Catalyzed C-H Aminations Applicable to Late-Stage Functionalization

Herein, we report an iridium-catalyzed directed C-H amination methodology developed using a high-throughput experimentation (HTE)-based strategy, applicable for the needs of automated modern drug discovery. The informer library approach for investigating the accessible directing group chemical space, in combination with functional group tolerance screening and substrate scope investigations, allowed for the generation of reaction application guidelines to aid future users. Applicability to late-stage functionalization of complex drugs and natural products, in combination with multiple deprotection protocols leading to the desirable aniline matched pairs, serve to demonstrate the utility of the method for drug discovery. Finally, reaction miniaturization to a nanomolar range highlights the opportunities for more sustainable screening with decreased material consumption.

3CL Protease Inhibitors with an Electrophilic Arylketone Moiety as Anti-SARS-CoV-2 Agents

The novel coronavirus, SARS-CoV-2, has been identified as the causative agent for the current coronavirus disease (COVID-19) pandemic. 3CL protease (3CLpro) plays a pivotal role in the processing of viral polyproteins. We report peptidomimetic compounds with a unique benzothiazolyl ketone as a warhead group, which display potent activity against SARS-CoV-2 3CLpro. The most potent inhibitor YH-53 can strongly block the SARS-CoV-2 replication. X-ray structural analysis revealed that YH-53 establishes multiple hydrogen bond interactions with backbone amino acids and a covalent bond with the active site of 3CLpro. Further results from computational and experimental studies, including an in vitro absorption, distribution, metabolism, and excretion profile, in vivo pharmacokinetics, and metabolic analysis of YH-53 suggest that it has a high potential as a lead candidate to compete with COVID-19.

XGBoost-based intelligence yield prediction and reaction factors analysis of amination reaction

Buchwald-Hartwig amination reaction catalyzed by palladium plays an important role in drug synthesis. In the last few years, machine learning-assisted strategies emerged and quickly gained attention. In this article, an importance and relevance-based integrated feature screening method is proposed to effectively filter high-dimensional feature descriptor data. Then, a regularized machine learning boosting tree model, eXtreme Gradient Boosting, is introduced to intelligently predict reaction performance in multidimensional chemistry space. Furthermore, convergence, interpretability, generalization, and the internal association between reaction conditions and yields are excavated, which provides intelligent assistance for the optimal design of coupling reaction system and evaluating the reaction conditions. Compared with recently published results, the proposed method requires fewer feature descriptors, takes less time, and achieves more accurate prediction accuracy.

Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)-C(sp3) bond formation. While many of these methods typically employ aryl bromides as the C(sp2) coupling partner, a variety of aliphatic radical sources have been investigated. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because nonstandardized sets of aryl bromides are used in scope evaluation. Herein, we report a Ni/photoredox-catalyzed (deutero)methylation and alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. Reaction development, mechanistic studies, and late-stage derivatization of a biologically relevant aryl chloride, fenofibrate, are presented. Then, we describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing scope examples from published Ni/photoredox methods on this same chemical space, we identify areas of sparse coverage and high versus low average yields, enabling comparisons between prior art and this new method. Additionally, we demonstrate that the systematically selected scope of aryl bromides can be used to quantify population-wide reactivity trends and reveal sources of possible functional group incompatibility with supervised machine learning.

A reactivity model for oxidative addition to palladium enables quantitative predictions for catalytic cross-coupling reactions

Making accurate, quantitative predictions of chemical reactivity based on molecular structure is an unsolved problem in chemical synthesis, particularly for complex molecules. We report an approach to reactivity prediction for...

Development of a high intensity parallel photoreactor for high throughput screening

Enhanced reactivity of decarboxylative cross-coupling using a new parallel photoreactor.

Tunable and Practical Homogeneous Organic Reductants for Cross-Electrophile Coupling

The syntheses of four new tunable homogeneous organic reductants based on a tetraaminoethylene scaffold are reported. The new reductants have enhanced air stability compared to current homogeneous reductants for metal-mediated reductive transformations, such as cross-electrophile coupling (XEC), and are solids at room temperature. In particular, the weakest reductant is indefinitely stable in air and has a reduction potential of -0.85 V versus ferrocene, which is significantly milder than conventional reductants used in XEC. All of the new reductants can facilitate C(sp²)-C(sp³) Ni-catalyzed XEC reactions and are compatible with complex substrates that are relevant to medicinal chemistry. The reductants span a range of nearly 0.5 V in reduction potential, which allows for control over the rate of electron transfer events in XEC. Specifically, we report a new strategy for controlled alkyl radical generation in Ni-catalyzed C(sp²)-C(sp³) XEC. The key to our approach is to tune the rate of alkyl radical generation from Katritzky salts, which liberate alkyl radicals upon single electron reduction, by varying the redox potentials of the reductant and Katritzky salt utilized in catalysis. Using our method, we perform XEC reactions between benzylic Katritzky salts and aryl halides. The method tolerates a variety of functional groups, some of which are particularly challenging for most XEC transformations. Overall, we expect that our new reductants will both replace conventional homogeneous reductants in current reductive transformations due to their stability and relatively facile synthesis and lead to the development of novel synthetic methods due to their tunability.

What is in Our Kit? An Analysis of Building Blocks Used in Medicinal Chemistry Parallel Libraries

Building blocks are the molecular foundations for drug molecule design. The building block is one of the determining factors of final compound qualities in any given medicinal chemistry campaign. Herein, we describe our analysis of the building blocks used in parallel library synthesis at AbbVie. The results gave insights into the synthetic tractability and accessibilities of building blocks used in medicinal chemistry. Furthermore, our analysis showed that opportunities still exist for the identification and future incorporation of underrepresented building blocks, even for commonly used reactions, to obtain intellectual and competitive advantages in drug discovery.