Deciphering complexity in Pd-catalyzed cross-couplings

Understanding complex reaction systems is critical in chemistry. While synthetic methods for selective formation of products are sought after, oftentimes it is the full reaction signature, i.e., complete profile of products/side-products, that informs mechanistic rationale and accelerates discovery chemistry. Here, we report a methodology using high-throughput experimentation and multivariate data analysis to examine the full signature of one of the most complicated chemical reactions catalyzed by palladium known in the chemical literature. A model Pd-catalyzed reaction was selected involving functionalization of 2-bromo-N-phenylbenzamide and multiple bond activation pathways. Principal component analysis, correspondence analysis and heatmaps with hierarchical clustering reveal the factors contributing to the variance in product distributions and show associations between solvents and reaction products. Using robust data from experiments performed with eight solvents, for four different reaction times at five different temperatures, we correlate side-products to a major dominant N-phenyl phenanthridinone product, and many other side products.

Rapid reaction optimization by robust and economical quantitative benchtop 19F NMR spectroscopy

The instrumental analysis of reaction mixtures is usually the rate-determining step in the optimization of chemical processes. Traditionally, reactions are analyzed by gas chromatography, HPLC or quantitative NMR spectroscopy on high-field spectrometers. However, chromatographic methods require elaborate work-up and calibration protocols, and high-field NMR spectrometers are expensive to purchase and operate. This protocol describes an inexpensive and highly effective analysis method based on low-field benchtop NMR spectroscopy. Its key feature is the use of fluorine-labeled model substrates that, because of the wide chemical shift range and high sensitivity of 19F, enable separate, quantitative detection of product and by-product signals even on low-field, permanent magnet spectrometers. An external lock/shim device obviates the need for deuterated solvents, permitting the direct, noninvasive measurement of crude reaction mixtures with minimal workup. The low field-strength facilitates a homogeneous excitation over a wide chemical shift range, minimizing systematic integration errors. The addition of the optimal amount of the nonshifting relaxation agent tris(acetylacetonato) iron(III) minimizes relaxation delays at full resolution, reducing the analysis time to 32 s per sample. The correct choice of processing parameters is also crucial. A step-by-step guideline is provided, the influence of all parameters, including adjustments needed when using high-field spectrometers, is discussed and potential pitfalls are highlighted. The wide applicability of the analytical protocol for reaction optimization is illustrated by three examples: a Buchwald-Hartwig amination, a Suzuki coupling and a C-H arylation reaction.

Deconvolution and Analysis of the 1H NMR Spectra of Crude Reaction Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity of manually analyzing the NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with the experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C-H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with a mean absolute error as low as 1%.

Baldwin and Whitehead's Manzamine Alkaloids Biosynthesis Hypothesis Involves a Finely Tuned Reactivity of Acrolein: Automated Extraction of Reactivity Patterns from LC-MS2 Data

Inspired by the multicomponent reaction-type scenario involving fatty dialdehydes, a nitrogen source, and acrolein, as a key C3 unit, put forward by Baldwin and Whitehead to explain the formation of manzamine-type alkaloids, 96 multicomponent reactions were designed, and their analytical readouts were deconvoluted using a herein-provided chemoinformatic workflow. This strategy pinpointed relevant conditions tuning the reactivity of acrolein to fulfill Baldwin and Whitehead's manzamine alkaloids biosynthetic hypothesis. This strategy can become part of a general method for the high-content analysis of multicomponent reactions applied to a natural product biosynthetic scenario.

Quantum chemical calculations for reaction prediction in the development of synthetic methodologies

Quantum chemical calculations have been used in the development of synthetic methodologies to analyze the reaction mechanisms of the developed reactions. Their ability to estimate chemical reaction pathways, including transition state energies and connected equilibria, has led researchers to embrace their use in predicting unknown reactions. This perspective highlights strategies that leverage quantum chemical calculations for the prediction of reactions in the discovery of new methodologies. Selected examples demonstrate how computation has driven the development of unknown reactions, catalyst design, and the exploration of synthetic routes to complex molecules prior to often laborious, costly, and time-consuming experimental investigations.

Molecular Thin Films Enable the Synthesis and Screening of Nanoparticle Megalibraries Containing Millions of Catalysts

Megalibraries are centimeter-scale chips containing millions of materials synthesized in parallel using scanning probe lithography. As such, they stand to accelerate how materials are discovered for applications spanning catalysis, optics, and more. However, a long-standing challenge is the availability of substrates compatible with megalibrary synthesis, which limits the structural and functional design space that can be explored. To address this challenge, thermally removable polystyrene films were developed as universal substrate coatings that decouple lithography-enabled nanoparticle synthesis from the underlying substrate chemistry, thus providing consistent lithography parameters on diverse substrates. Multi-spray inking of the scanning probe arrays with polymer solutions containing metal salts allows patterning of >56 million nanoreactors designed to vary in composition and size. These are subsequently converted to inorganic nanoparticles via reductive thermal annealing, which also removes the polystyrene to deposit the megalibrary. Megalibraries with mono-, bi-, and trimetallic materials were synthesized, and nanoparticle size was controlled between 5 and 35 nm by modulating the lithography speed. Importantly, the polystyrene coating can be used on conventional substrates like Si/SiO_x, as well as substrates typically more difficult to pattern on, such as glassy carbon, diamond, TiO_2, BN, W, or SiC. Finally, high-throughput materials discovery is performed in the context of photocatalytic degradation of organic pollutants using Au-Pd-Cu nanoparticle megalibraries on TiO₂ substrates with 2,250,000 unique composition/size combinations. The megalibrary was screened within 1 h by developing fluorescent thin-film coatings on top of the megalibrary as proxies for catalytic turnover, revealing Au_0.53Pd_0.38Cu_0.09-TiO₂ as the most active photocatalyst composition.

An update on late-stage functionalization in today's drug discovery

INTRODUCTION

Late-stage functionalization (LSF) allows for the introduction of new chemical groups toward the end of a synthetic sequence, which means new molecules can be rapidly accessed without laborious de novo chemical synthesis. Over the last decade, medicinal chemists have begun to implement LSF strategies into their drug discovery programs, affording benefits such as efficient access to diverse libraries to explore structure-activity relationships and the improvement of physicochemical and pharmacokinetic properties.

AREAS COVERED

An overview of the key advancements in LSF methodology development from 2019 to 2022 and their applicability to drug discovery is provided. In addition, several examples from both academia and industry where LSF methodologies have been applied by medicinal chemists to their drug discovery programs are presented.

EXPERT OPINION

Utilization of LSF by medicinal chemists is on the rise, both in academia and in industry. The maturation of the LSF field to produce methodologies bearing increased regioselectivity, scope, and functional group tolerance is envisaged to narrow the gap between methodology development and medicinal chemistry research. The authors predict that the sheer versatility of these techniques in facilitating challenging chemical transformations of bioactive molecules will continue to increase the efficiency of the drug discovery process.

A Brief Introduction to Chemical Reaction Optimization

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

Mechanistic Investigation into the Regio-Controllable Hydroallylations of Alkynes with Allylborons under Pd-Based Synergetic Catalyses

Density functional theory calculations were employed to investigate the Pd-catalyzed regio-selective hydroallylations of alkynes with allylborons: cooperation of Cu(OAc)₂ and dppe resulting in 1,4-dienes while combination of AdCO₂H and PCy₃ leading to 1,5-dienes. A unified rationalization mechanism called "Lewis-acid-base-interaction promoted deprotonation/3,3-rearrangement" was proposed. Compared with the commonly reported metathesis pathway to only afford the metal-allyl intermediate, in the newly established mechanism, an additional Brønsted acid (as an initiator of the Pd⁰ oxidative addition) is generated by the interaction of the allylboron (Lewis acid) B atom with the ⁿBuOH (Lewis base) O atom, and subsequent 3,3-rearrangement ensures the thermodynamic feasibility of the reaction. In addition, it was found that excess Cu(OAc)₂ plays two potential roles in the oxidative addition/alkyne insertion: (i) the participation of one AcO^- of Cu(OAc)₂ ensures a large orbital overlap between the migrating H and Pd atoms, facilitating the formal AcO-H cleavage and (ii) the extra (OAc)₂Cu···O(carboxyl) σ-coordination indirectly contributes to the (Me)C≡C(Ph) insertion into the Pd-H bond. Further analysis showed that the origin of the regioselectivity is closely related to the employed phosphorus ligand. These revealed results, which have been overlooked in the previous documents, would aid the development of new related catalytic reactions.

CO2-Promoted and Nickel-Catalyzed Direct Hydroallylation of Terminal Alkynes with Allylic Alcohols: Access to 1,4-Dienes

CO₂-promoted and Ni-catalyzed direct hydroallylation of terminal alkynes with allylic alcohols has been achieved. Various 1,4-dienes could be synthesized in good yield with excellent Markovnikov selectivity for alkyl- and aryl-substituted terminal alkynes under mild reaction conditions. A gram-scale reaction gives considerable yield. Preliminary mechanistic studies support the reaction pathway through sequential carboxylation/allylnickelation/lithium bicarbonate nickelation/transmetalation in the hydroallylation of alkynes with allylic alcohols in the presence of CO₂.

Comparative Screening of DalPhos/Ni Catalysts in C-N Cross-couplings of (Hetero)aryl Chlorides Enables Development of Aminopyrazole Cross-couplings with Amine Base

A systematic competitive evaluation of the DalPhos ligand family in nickel-catalyzed N-arylation chemistry is reported, involving primary (linear and branched) and secondary alkylamines, as well as a primary five-membered heteroarylamine (aminopyrazole), in combination with a diverse set of test electrophiles and bases (NaOtBu, K₂ CO₃ , DBU/NaTFA). In addition to providing optimal ligand/catalyst identification, and bringing to light methodology limitations (e. g., unwanted C-O cross-coupling with NaOtBu), our survey enabled the development of the first efficient catalyst system for heteroatom-dense C-N cross-coupling of aminopyrazoles and related nucleophiles with (hetero)aryl chlorides by use of an amine 'dual-base' system.

Machine-Learning-Guided Discovery of Electrochemical Reactions

The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.

High-Throughput Platform for Novel Reaction Discovery

Detecting the formation of new chemical bonds in high-throughput synthesis is limited by the efficiency and scalability of reaction product detection, as conventional methods for isolating product from reaction mixtures are time consuming and labor intensive. Here, we report a miniaturizable purification method that enables the rapid, high-throughput isolation of quaternary ammonium-tagged products from reaction mixtures with excellent purity using inexpensive equipment that easily can be set up in a typical organic chemistry laboratory. This novel purification technique enabled us to establish a high-throughput reaction discovery platform. We validated this platform in a screen of 1536 reactions, and one previously unreported transformation was identified.

Biomacromolecule-Assisted Screening for Reaction Discovery and Catalyst Optimization

Reaction discovery and catalyst screening lie at the heart of synthetic organic chemistry. While there are efforts at de novo catalyst design using computation/artificial intelligence, at its core, synthetic chemistry is an experimental science. This review overviews biomacromolecule-assisted screening methods and the follow-on elaboration of chemistry so discovered. All three types of biomacromolecules discussed─enzymes, antibodies, and nucleic acids─have been used as "sensors" to provide a readout on product chirality exploiting their native chirality. Enzymatic sensing methods yield both UV-spectrophotometric and visible, colorimetric readouts. Antibody sensors provide direct fluorescent readout upon analyte binding in some cases or provide for cat-ELISA (Enzyme-Linked ImmunoSorbent Assay)-type readouts. DNA biomacromolecule-assisted screening allows for templation to facilitate reaction discovery, driving bimolecular reactions into a pseudo-unimolecular format. In addition, the ability to use DNA-encoded libraries permits the barcoding of reactants. All three types of biomacromolecule-based screens afford high sensitivity and selectivity. Among the chemical transformations discovered by enzymatic screening methods are the first Ni(0)-mediated asymmetric allylic amination and a new thiocyanopalladation/carbocyclization transformation in which both C-SCN and C-C bonds are fashioned sequentially. Cat-ELISA screening has identified new classes of sydnone-alkyne cycloadditions, and DNA-encoded screening has been exploited to uncover interesting oxidative Pd-mediated amido-alkyne/alkene coupling reactions.

Selected Ion Monitoring Using Low-Cost Mass Spectrum Detectors Provides a Rapid, General, and Accurate Method for Enantiomeric Excess Determination in High-Throughput Experimentation

High-Throughput Experimentation (HTE) workflows are efficient means of surveying a broad array of chiral catalysts in the development of catalytic asymmetric reactions. However, use of traditional HPLC-UV/vis methodology to determine enantiomeric excess (ee) from the resulting reactions is often hampered by co-elution of other reaction components, resulting in erroneous ee determination when crude samples are used, and ultimately requiring product isolation prior to ee analysis. In this study, using four published reactions selected as model systems, we demonstrate that the use of LC-MS, SFC-MS, and selected ion monitoring (SIM) mass chromatography provides a highly accurate means to determine ee of products in crude reaction samples using commonplace, low-cost MS detectors. By using ion selection, co-eluting signals can be deconvoluted to provide accurate integrations of the target analytes. We also show that this method is effective for samples lacking UV/vis chromophores, making it ideal for HTE workflows in asymmetric catalysis.

A "Pool and Split" Approach to the Optimization of Challenging Pd-Catalyzed C-N Cross-Coupling Reactions

A screening method for the rapid identification of catalytic conditions for Pd-catalyzed C-N cross-coupling reactions is reported. The strategy evaluates mixtures of precatalysts, ligands, and bases to identify productive conditions that are subsequently optimized through two deconvolution steps, which uncover the active catalyst and identify the optimal solvent and base for the catalytic system. The efficacy of this approach was demonstrated through application to a previously reported reaction, whereby both the literature conditions and additional solutions were retrieved. The same approach to Ni-catalyzed C-N cross-coupling was investigated in parallel but was found to be less successful due to limited activity of the evaluated reagent combinations. Finally, the utility of this method was showcased by identifying effective conditions for the Pd-catalyzed cross-coupling of complex molecules, which not only revealed nonobvious solutions for the processes under evaluation but also resulted in the discovery of new chemical reactions.

Technological Innovations in Photochemistry for Organic Synthesis: Flow Chemistry, High-Throughput Experimentation, Scale-up, and Photoelectrochemistry

Photoinduced chemical transformations have received in recent years a tremendous amount of attention, providing a plethora of opportunities to synthetic organic chemists. However, performing a photochemical transformation can be quite a challenge because of various issues related to the delivery of photons. These challenges have barred the widespread adoption of photochemical steps in the chemical industry. However, in the past decade, several technological innovations have led to more reproducible, selective, and scalable photoinduced reactions. Herein, we provide a comprehensive overview of these exciting technological advances, including flow chemistry, high-throughput experimentation, reactor design and scale-up, and the combination of photo- and electro-chemistry.

Palladium-Catalyzed Cascade Allylative Dicarbofunctionalization of Aryl Phenol-Tethered Alkynes with Allyl Iodides: Synthesis of Skipped Dienes

A palladium-catalyzed cascade allylative dicarbofunctionalization of aryl phenol-tethered alkynes with allyl iodides is described. A series of polysubstituted spirocyclo-containing skipped dienes with an all-carbon tetrasubstituted alkene unit are synthesized through this convenient process. The cascade reaction proceeds selectively through dearomative C-allylation instead of O-allylation of aryl phenols.

Towards Data-driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time- and resource-consuming process due to the lack of predictive catalyst design strategy. Targeting the data-driven design of asymmetric catalysis, we herein report the development of a standardized database that contains the detailed information of over 12000 literature asymmetric hydrogenations of olefins. This database provides a valuable platform for the machine learning applications in asymmetric catalysis. Based on this database, we developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem and will facilitate the reaction optimization with new olefin substrate in catalysis screening.

Glass surface as strong base, 'green' heterogeneous catalyst and degradation reagent

Systematic screening of accelerated chemical reactions at solid/solution interfaces has been carried out in high-throughput fashion using desorption electrospray ionization mass spectrometry and it provides evidence that glass surfaces accelerate various base-catalyzed chemical reactions. The reaction types include elimination, solvolysis, condensation and oxidation, whether or not the substrates are pre-charged. In a detailed mechanistic study, we provide evidence using nanoESI showing that glass surfaces can act as strong bases and convert protic solvents into their conjugate bases which then act as bases/nucleophiles when participating in chemical reactions. In aprotic solvents such as acetonitrile, glass surfaces act as 'green' heterogeneous catalysts that can be recovered and reused after simple rinsing. Besides their use in organic reaction catalysis, glass surfaces are also found to act as degradation reagents for phospholipids with increasing extents of degradation occurring at low concentrations. This finding suggests that the storage of base/nucleophile-labile compounds or lipids in glass containers should be avoided.

Late-stage C–H functionalization offers new opportunities in drug discovery

Over the past decade, the landscape of molecular synthesis has gained major impetus by the introduction of late-stage functionalization (LSF) methodologies. C-H functionalization approaches, particularly, set the stage for new retrosynthetic disconnections, while leading to improvements in resource economy. A variety of innovative techniques have been successfully applied to the C-H diversification of pharmaceuticals, and these key developments have enabled medicinal chemists to integrate LSF strategies in their drug discovery programmes. This Review highlights the significant advances achieved in the late-stage C-H functionalization of drugs and drug-like compounds, and showcases how the implementation of these modern strategies allows increased efficiency in the drug discovery process. Representative examples are examined and classified by mechanistic patterns involving directed or innate C-H functionalization, as well as emerging reaction manifolds, such as electrosynthesis and biocatalysis, among others. Structurally complex bioactive entities beyond small molecules are also covered, including diversification in the new modalities sphere. The challenges and limitations of current LSF methods are critically assessed, and avenues for future improvements of this rapidly expanding field are discussed. We, hereby, aim to provide a toolbox for chemists in academia as well as industrial practitioners, and introduce guiding principles for the application of LSF strategies to access new molecules of interest.

Dehydrative Allylation of Alkenyl sp2 C-H Bonds

We designed a cooperative catalytic system by combining commercially available Ca(NTf₂)PF₆ and Pd(PPh₃)₄ to address the dehydrative allylation of alkenyl sp² C-H bonds in an environmentally benign manner. A novel C-OH bond cleavage method was found to be crucial for this practical protocol. A variety of alkenes and allylic alcohols equipped with wide-spectrum functional groups can be successfully incorporated into the desired cross-coupling, affording 1,4-dienes with moderate to excellent yields and high stereo- and regioselectivity.

Nanoscale, automated, high throughput synthesis and screening for the accelerated discovery of protein modifiers

Hit finding in early drug discovery is often based on high throughput screening (HTS) of existing and historical compound libraries, which can limit chemical diversity, is time-consuming, very costly, and environmentally not sustainable. On-the-fly compound synthesis and in situ screening in a highly miniaturized and automated format has the potential to greatly reduce the medicinal chemistry environmental footprint. Here, we used acoustic dispensing technology to synthesize a library in a 1536 well format based on the Groebcke-Blackburn-Bienaymé reaction (GBB-3CR) on a nanomole scale. The unpurified library was screened by differential scanning fluorimetry (DSF) and cross-validated using microscale thermophoresis (MST) against the oncogenic protein-protein interaction menin-MLL. Several GBB reaction products were found as μM menin binder, and the structural basis of the interactions with menin was elucidated by co-crystal structure analysis. Miniaturization and automation of the organic synthesis and screening process can lead to an acceleration in the early drug discovery process, which is an alternative to classical HTS and a step towards the paradigm of continuous manufacturing.

Reaction screening in multiwell plates: high-throughput optimization of a Buchwald-Hartwig amination

Chemical space is vast, and chemical reactions involve the complex interplay of multiple variables. As a consequence, reactions can fail for subtle reasons, necessitating screening of conditions. High-throughput experimentation (HTE) techniques enable a more comprehensive array of data to be obtained in a relatively short amount of time. Although HTE can be most efficiently achieved with automated robotic dispensing equipment, the benefits of running reaction microarrays can be accessed in any regularly equipped laboratory using inexpensive consumables. Herein, we present a cost-efficient approach to HTE, examining a Buchwald-Hartwig amination as our model reaction. Experiments are carried out in a machined aluminum 96-well plate, taking advantage of solid transfer scoops and pipettes to facilitate rapid reagent transfer. Reaction vials are simultaneously heated and mixed, using a magnetic stirrer, and worked up in parallel, using a plastic filter plate. Analysis by gas chromatography provides the chemist with 96 data points with minimal commitment of time and resources. The best-performing experiment can be selected for scale-up and isolation, or the data can be used for designing future optimization experiments.

A Validated "Pool and Split" Approach to Screening and Optimization of Copper-Catalyzed C-N Cross-Coupling Reactions

A general method for the quick identification of effective catalytic systems for copper-catalyzed C-N cross-couplings is described. This is based on evaluating mixtures of copper sources, ancillary ligands, and bases in different solvents followed by two deconvolution procedures, which aim at identifying the most proper reagent combination in only three distinct steps. Despite being a high-throughput approach in nature, the proposed method utilizes only frugal technological platforms such as 24-well microplates while offering a screening efficiency-the number of executed experiments vs the total number of possible experiments-higher than 95%. To facilitate visualization and mining of the high-throughput experimentation (HTE) data, Visual Basic scripts have been developed, which allow streamlining the extraction of raw HPLC data into TIBCO Spotfire for the graphical display in the form of pie charts. The unique capabilities of this "pool and split" approach have been demonstrated by applying it to literature known cross-coupling reactions. In every case, the described experimental setup was validated by retrieving the original literature conditions in addition to exposing several additional solutions with a minimum number of parallel experiments. Finally, examples are provided for the successful application of this HTE screening workflow to internal projects.

Beyond hydrogen bonding: recent trends of outer sphere interactions in transition metal catalysis

The implementation of interactions beyond hydrogen bonding in the 2^nd coordination sphere of transition metal catalysts is rare. However, it has already shown great promise in last 5 years, providing new tools to control the activity and selectivity as here reviewed.

C=C-Ene-Reductases Reduce the C=N Bond of Oximes

Although enzymes have been found for many reactions, there are still transformations for which no enzyme is known. For instance, not a single defined enzyme has been described for the reduction of the C=N bond of an oxime, only whole organisms. Such an enzymatic reduction of an oxime may give access to (chiral) amines. By serendipity, we found that the oxime moiety adjacent to a ketone as well as an ester group can be reduced by ene-reductases (ERs) to an intermediate amino group. ERs are well-known enzymes for the reduction of activated alkenes, as of α,β-unsaturated ketones. For the specific substrate used here, the amine intermediate spontaneously reacts further to tetrasubstituted pyrazines. This reduction reaction represents an unexpected promiscuous activity of ERs expanding the toolkit of transformations using enzymes.

Elucidating the Weakly Reversible Cs-Pb-Br Perovskite Nanocrystal Reaction Network with High-Throughput Maps and Transformations

Advances in automation and data analytics can aid exploration of the complex chemistry of nanoparticles. Lead halide perovskite colloidal nanocrystals provide an interesting proving ground: there are reports of many different phases and transformations, which has made it hard to form a coherent conceptual framework for their controlled formation through traditional methods. In this work, we systematically explore the portion of Cs-Pb-Br synthesis space in which many optically distinguishable species are formed using high-throughput robotic synthesis to understand their formation reactions. We deploy an automated method that allows us to determine the relative amount of absorbance that can be attributed to each species in order to create maps of the synthetic space. These in turn facilitate improved understanding of the interplay between kinetic and thermodynamic factors that underlie which combination of species are likely to be prevalent under a given set of conditions. Based on these maps, we test potential transformation routes between perovskite nanocrystals of different shapes and phases. We find that shape is determined kinetically, but many reactions between different phases show equilibrium behavior. We demonstrate a dynamic equilibrium between complexes, monolayers, and nanocrystals of lead bromide, with substantial impact on the reaction outcomes. This allows us to construct a chemical reaction network that qualitatively explains our results as well as previous reports and can serve as a guide for those seeking to prepare a particular composition and shape.

Autonomous Discovery in the Chemical Sciences Part II: Outlook

This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to "discover" despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.

Catalytic Intramolecular Conjugate Additions of Aldehyde-Derived Enamines to α,β-Unsaturated Esters

We report a pairing of known catalysts that enables intramolecular conjugate additions of aldehyde-derived enamines to α,β-unsaturated esters. Despite extensive prior exploration of conjugate additions of aldehyde-derived enamines, catalytic conjugate additions to unactivated enoate esters are unprecedented. Achieving enantioselective and diastereoselective six-membered ring formation requires the coordinated action of a chiral pyrrolidine, for nucleophilic activation of the aldehyde via enamine formation, and a hydrogen bond donor, for electrophilic activation of the enoate ester. Proper selection of the hydrogen bond donor is essential for chemoselectivity, which requires minimizing competition from homoaldol reaction. Utility is demonstrated in a six-step synthesis of (-)-yohimbane from cycloheptene.

High-Throughput Experimentation and Continuous Flow Evaluation of Nucleophilic Aromatic Substitution Reactions

Nucleophilic aromatic substitution (S_NAr) reactions were optimized using high-throughput experimentation techniques for execution under flow conditions. A total of 3072 unique reactions were evaluated with an analysis time of ∼3.5 s per reaction using a system that combines a liquid handling robot for reaction mixture preparation with desorption electrospray ionization (DESI) mass spectrometry (MS) for analysis. The reactions were performed in bulk microtiter arrays with and without incubation. In-house developed software was used to process the data and generate heat maps of the results. This information was then used to select the most promising conditions for continuous synthesis under microfluidic reactor conditions. Our results show that this HTE approach provides robust guidance for narrowing the range of conditions needed for optimization of S_NAr reactions.

Machine learning for mixture toxicity analysis based on high-throughput printing technology

Analysis on mixture toxicity (Mix-tox) of the multi-chemical space is constantly followed with interest for many researchers. Conventional toxicity tests with time-consuming and costly operations make researchers can only establish some toxicity prediction models aiming to a limited sampling dimension. The rapid development of machine learning (ML) algorithm will accelerate the exploration of many fields involving toxicity analysis. Rather than the model calculation capacity, the challenge of this process mainly comes from the lack of toxicology big-data to perform toxicity perception through the ML model. In this paper, a full strategy based a standardized high-throughput experiment was developed for Mix-tox analysis throughout the whole routine, from big-sample dataset design, model building, and training, to the toxicity prediction. Using the concentration variates as input and bio-luminescent inhibition rate as output, it turned out that a well-trained random forest algorithm was successfully applied to assess the mixtures' toxicity effect, suggesting its value in facilitating adoption of Mix-tox analysis.

Fast-developing machine learning support complex system research in environmental chemistry

Machine learning will radically accelerate analysis of complex material networks in environmental chemistry.

Accelerated Discovery in Photocatalysis by a Combined Screening Approach Involving MS Tags

Herein we report on the development of an MS tag screening strategy that accelerates the discovery of photocatalytic reactions. By efficiently combining mechanism- and reaction-based screening dimensions, the respective advantages of each strategy were retained, whereas the drawbacks inherent to each screening approach could be eliminated. Applying this approach led to the discovery of a mild photosensitized decarboxylative hydrazide synthesis from mesoionic sydnones and carboxylic acids as starting materials.

High Throughput Experimentation Using DESI-MS to Guide Continuous-Flow Synthesis

We demonstrate the use of accelerated reactions with desorption electrospray ionization mass spectrometry (DESI-MS) as a tool for predicting the outcome of microfluidic reactions. DESI-MS was employed as a high throughput experimentation tool to provide qualitative predictions of reaction outcomes, so that vast regions of chemical reactivity space may be more rapidly explored and areas of optimal efficiency identified. This work is part of a larger effort to accelerate reaction optimization to enable the rapid development of continuous-flow syntheses of small molecules in high yield. In order to build confidence in this approach, however, it is necessary to establish a robust predictive connection between reactions performed under analogous DESI-MS, batch, and microfluidic reaction conditions. In the present work, we explore the potential of high throughput DESI-MS experiments to identify trends in reactivity based on chemical structure, solvent, temperature, and stoichiometry that are consistent across these platforms. N-alkylation reactions were used as the test case due to their ease of reactant and product detection by electrospray ionization mass spectrometry (ESI-MS) and their great importance in API synthesis. While DESI-MS narrowed the scope of possibilities for reaction selection among some parameters such as solvent, others like stoichiometry and temperature still required further optimization under continuous synthesis conditions. DESI-MS high throughput experimentation (HTE) reaction evaluation significantly reduced the search space for flow chemistry optimization, thus representing a significant savings in time and materials to achieve a desired transformation with high efficiency.

A regioselectivity switch in Pd-catalyzed hydroallylation of alkynes

By exploiting the reactivity of a vinyl-Pd species, we control the regioselectivity in hydroallylation of alkynes under Pd-hydride catalysis. A monophosphine ligand and carboxylic acid combination promotes 1,5-dienes through a pathway involving isomerization of alkynes to allenes. In contrast, a bisphosphine ligand and copper cocatalyst favor 1,4-dienes via a mechanism that involves transmetalation. Our study highlights how to access different isomers by diverting a common organometallic intermediate.