Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS

Strategies for automated affinity purification-resin screening for non-traditional biopharmaceuticals in the discovery space

Biotherapeutics occupy a significant portion of the pharmaceutical pipeline and are projected to continue growing in sales and scope. Further, the field is advancing novel and more complex molecules beyond monoclonal antibodies including multi-target proteins, engineered proteins and bioconjugates. In this aspect, the development of increasingly advanced and challenging therapies necessitates a commiserate degree of innovation to develop automated methods for resin screening, purification, and analytics in the discovery space to quickly identify liabilities and rank candidates with minimal impact on developmental resources. In this work, we introduce an automated resin screening platform tailored to small scale production runs for clone evaluation and process development in the biologics discovery space. The complex characteristics of these novel therapies requires empirical testing of resin to ensure optimal recovery of high-quality material for evaluation to inform on cell line development and future downstream process and analytical method development. This workflow enables the purification of milligrams of protein material for analytical testing and identifies ideal resins to leverage downstream as a candidate quickly progresses. This workflow was validated using a research monoclonal antibody and applied to a novel bispecific fusion protein to evaluate resin performance with respect to recovery, purity and impact on higher-order structure.

Rapid Exploration of Chemical Space by High-Throughput Desorption Electrospray Ionization Mass Spectrometry

This study leverages accelerated reactions at the solution/air interface of microdroplets generated by desorption electrospray ionization (DESI) to explore the chemical space. DESI is utilized to synthesize drug analogs at an overall rate of 1 reaction mixture per second, working on the low-nanogram scale. Transformations of multiple drug molecules at specific functionalities (phenol, hydroxyl, amino, carbonyl, phenyl, thiophenyl, and alkenyl) are achieved using electrophilic/nucleophilic, redox, C-H functionalization, and coupling reactions. These transformations occur under ambient conditions on the millisecond time scale with direct detection of products being successful in all but three of the reaction types studied. The large scope (22 bioactive compounds, >20 chemical transformations, and >300 functionalization reagents) and high speed (>3000 reactions/hour) provide access to a wide array of drug analogs that can be used for bioactivity testing. A total of ∼6800 unique reactions were examined through a data-driven workflow, and more than 3000 unique derivatives (∼44%) were identified tentatively by the m/z value and signal-to-control ratio in single-stage mass spectrometry (MS) analysis, with over 1000 being further characterized by tandem MS. The speed of the DESI-MS reaction screen provides potential advantages for emerging machine learning-based predictions of organic synthesis, and it sets the stage for future online DESI-MS bioassays and scaled-up microdroplet synthesis before formal characterization of hit compounds is sought using traditional methods of drug discovery.

Continuous collective analysis of chemical reactions

The automated synthesis of small organic molecules from modular building blocks has the potential to transform our capacity to create medicines and materials1-3. Disruptive acceleration of this molecule-building strategy broadly unlocks its functional potential and requires the integration of many new assembly chemistries. Although recent advances in high-throughput chemistry4-6 can speed up the development of appropriate synthetic methods, for example, in selecting appropriate chemical reaction conditions from the vast range of potential options, equivalent high-throughput analytical methods are needed. Here we report a streamlined approach for the rapid, quantitative analysis of chemical reactions by mass spectrometry. The intrinsic fragmentation features of chemical building blocks generalize the analyses of chemical reactions, allowing sub-second readouts of reaction outcomes. Central to this advance was identifying that starting material fragmentation patterns function as universal barcodes for downstream product analysis by mass spectrometry. Combining these features with acoustic droplet ejection mass spectrometry7,8 we could eliminate slow chromatographic steps and continuously evaluate chemical reactions in multiplexed formats. This enabled the assignment of reaction conditions to molecules derived from ultrahigh-throughput chemical synthesis experiments. More generally, these results indicate that fragmentation features inherent to chemical synthesis can empower rapid data-rich experimentation.

Rapid Aminations of Functionalized Aryl Fluorosulfates in Water

Aryl fluorosulfates of varying complexities have been used in amination reactions in water using a new Pd oxidative addition complex (OAC-1) developed specifically to match the needs of the fine chemicals industry, not only in terms of functional group tolerance, but also reflecting time considerations associated with these important C-N couplings. Also especially noteworthy is that they replace both PFAS-related triflates and nonaflates, which are today out of favor due to recent government regulations. The new complex based on the BippyPhos ligand is used at low loadings and under aqueous micellar conditions. Moreover, it is easily prepared and stable to long term storage. DFT calculations on the OAC precatalyst compare well with the X-ray structure of the crystals with π-complexation to the aromatic system of the ligand and also confirm the NMR data showing a mixture of conformers in solution that differ from the X-ray structure in rotation of the phenyl and t-butyl ligand substituents. An extensive variety of coupling partners, including pharmaceutically relevant APIs, readily participate under mild and environmentally responsible reaction conditions.

Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day

The current throughput of conventional organic chemical synthesis is usually a few experiments for each operator per day. We develop a robotic system for ultra-high-throughput chemical synthesis, online characterization, and large-scale condition screening of photocatalytic reactions, based on the liquid-core waveguide, microfluidic liquid-handling, and artificial intelligence techniques. The system is capable of performing automated reactant mixture preparation, changing, introduction, ultra-fast photocatalytic reactions in seconds, online spectroscopic detection of the reaction product, and screening of different reaction conditions. We apply the system in large-scale screening of 12,000 reaction conditions of a photocatalytic [2 + 2] cycloaddition reaction including multiple continuous and discrete variables, reaching an ultra-high throughput up to 10,000 reaction conditions per day. Based on the data, AI-assisted cross-substrate/photocatalyst prediction is conducted.

Adaptive Photochemical Amination via Co(II) Catalysis

Transition-metal-catalyzed amination of aryl halides is one of the most employed methods for constructing N-arylation adducts. However, the broad success of these reactions largely relies on the screening of precatalysts, elaborated ligands, and case-by-case optimization of reaction conditions (solvent, base, additive, temperature, etc.) for electronically or structurally diverse nucleophiles. Herein, we report an adaptive photochemical C-N coupling of aryl halides with various nitrogen nucleophiles (aliphatic and aromatic amines, amides, sulfonamides, pyrazoles, and ammonium salts) by Co(II) catalysis under the same reaction conditions (same precatalyst, same ligand, same base, same solvent, same temperature) without the addition of any exogenous photocatalyst. This photochemical amination features a wide substrate scope (>130 examples, up to 95% yield) with excellent functional group tolerance. Mechanistic studies indicate that these C-N coupling reactions may proceed via a Co(I)/Co(III) catalytic cycle.

Data processing for high-throughput mass spectrometry in drug discovery

INTRODUCTION

High-throughput mass spectrometry that could deliver > 10 times faster sample readout speed than traditional LC-based platforms has emerged as a powerful analytical technique, enabling the rapid analysis of complex biological samples. This increased speed of MS data acquisition has brought a critical demand for automatic data processing capabilities that should match or surpass the speed of data acquisition. Those data processing capabilities should serve the different requirements of drug discovery workflows.

AREAS COVERED

This paper introduced the key steps of the automatic data processing workflows for high-throughput MS technologies. Specific examples and requirements are detailed for different drug discovery applications.

EXPERT OPINION

The demand for automatic data processing in high-throughput mass spectrometry is driven by the need to keep pace with the accelerated speed of data acquisition. The seamless integration of processing capabilities with LIMS, efficient data review mechanisms, and the exploration of future features such as real-time feedback, automatic method optimization, and AI model training is crucial for advancing the drug discovery field. As technology continues to evolve, the synergy between high-throughput mass spectrometry and intelligent data processing will undoubtedly play a pivotal role in shaping the future of high-throughput drug discovery applications.

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.

High-Throughput Capillary Liquid Chromatography Using a Droplet Injection and Application to Reaction Screening

The cycle time of a standard liquid chromatography (LC) system is the sum of the time for the chromatographic run and the autosampler injection sequence. Although LC separation times in the 1-10 s range have been demonstrated, injection sequences are commonly >15 s, limiting throughput possible with LC separations. Further, such separations are performed on relatively large bore columns requiring flow rates of ≥5 mL/min, thus generating large volumes of mobile phase waste when used for large scale screening and increasing the difficulty in interfacing to mass spectrometry. Here, a droplet injector system was established that replaces the autosampler with a four-port, two-position valve equipped with a 20 nL internal loop interfaced to a syringe pump and a three-axis positioner to withdraw sample droplets from a well plate. In the system, sample and immiscible fluid are pulled alternately from a well plate into a capillary and then through the injection valve. The valve is actuated when sample fills the loop to allow sequential injection of samples at high throughput. Capillary LC columns with 300 μm inner diameter were used to reduce the consumption of mobile phase and sample. The system achieved 96 separations of 20 nL droplet samples containing 3 components in as little as 8.1 min with 5-s cycle time. This system was coupled to a mass spectrometer through an electrospray ionization source for high-throughput chemical reaction screening.

Computer-assisted multifactorial method development for the streamlined separation and analysis of multicomponent mixtures in (Bio)pharmaceutical settings

The (bio)pharmaceutical industry is rapidly moving towards complex drug modalities that require a commensurate level of analytical enabling technologies that can be deployed at a fast pace. Unsystematic method development and unnecessary manual intervention remain a major barrier towards a more efficient deployment of meaningful analytical assay across emerging modalities. Digitalization and automation are key to streamline method development and enable rapid assay deployment. This review discusses the use of computer-assisted multifactorial chromatographic method development strategies for fast-paced downstream characterization and purification of biopharmaceuticals. Various chromatographic techniques such as reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), ion exchange chromatography (IEX), hydrophobic interaction chromatography (HIC), and supercritical fluid chromatography (SFC) are addressed and critically reviewed. The most significant parameters for retention mechanism modelling, as well as mapping the separation landscape for optimal chromatographic selectivity and resolution are also discussed. Furthermore, several computer-assisted approaches for optimization and development of chromatographic methods of therapeutics, including linear, nonlinear, and multifactorial modelling are outlined. Finally, the potential of the chromatographic modelling and computer-assisted optimization strategies are also illustrated, highlighting substantial productivity improvements, and cost savings while accelerating method development, deployment and transfer processes for therapeutic analysis in industrial settings.

Ultra-high-throughput mass spectrometry in drug discovery: fundamentals and recent advances

INTRODUCTION

Ultra-high-throughput mass spectrometry, uHT-MS, is a technology that utilizes ionization and sample delivery technologies optimized to enable sampling from well plates at > 1 sample per second. These technologies do not need a chromatographic separation step and can be utilized in a wide variety of assays to detect a broad range of analytes including small molecules, lipids, and proteins.

AREAS COVERED

This manuscript provides a brief historical review of high-throughput mass spectrometry and the recently developed technologies that have enabled uHT-MS. The report also provides examples and references on how uHT-MS has been used in biochemical and chemical assays, nuisance compound profiling, protein analysis and high throughput experimentation for chemical synthesis.

EXPERT OPINION

The fast analysis time provided by uHT-MS is transforming how biochemical and chemical assays are performed in drug discovery. The potential to associate phenotypic responses produced by 1000's of compound treatments with changes in endogenous metabolite and lipid signals is becoming feasible. With the augmentation of simple, fast, high-throughput sample preparation, the scope of uHT-MS usage will increase. However, it likely will not supplant LC-MS for analyses that require low detection limits from complex matrices or characterization of complex biotherapeutics such as antibody-drug conjugates.

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.

Enhanced Structure-Based Prediction of Chiral Stationary Phases for Chromatographic Enantioseparation from 3D Molecular Conformations

The accurate prediction of suitable chiral stationary phases (CSPs) for resolving the enantiomers of a given compound poses a significant challenge in chiral chromatography. Previous attempts at developing machine learning models for structure-based CSP prediction have primarily relied on 1D SMILES strings [the simplified molecular-input line-entry system (SMILES) is a specification in the form of a line notation for describing the structure of chemical species using short ASCII strings] or 2D graphical representations of molecular structures and have met with only limited success. In this study, we apply the recently developed 3D molecular conformation representation learning algorithm, which uses rapid conformational analysis and point clouds of atom positions in the 3D space, enabling efficient chemical structure-based machine learning. By harnessing the power of the rapid 3D molecular representation learning and a data set comprising over 300,000 chromatographic enantioseparation records sourced from the literature, our models afford notable improvements for the chemical structure-based choice of appropriate CSP for enantioseparation, paving the way for more efficient and informed decision-making in the field of chiral chromatography.

Identifying general reaction conditions by bandit optimization

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.

Integrated Direct-to-Biology Platform for the Nanoscale Synthesis and Biological Evaluation of PROTACs

Proteolysis targeting chimeras (PROTACs) are heterobifunctional molecules that co-opt the cell's natural proteasomal degradation mechanisms to degrade undesired proteins. A challenge associated with PROTACs is the time and resource-intensive optimization; thus, the development of high-throughput platforms for their synthesis and biological evaluation is required. In this study, we establish an ultra-high-throughput experimentation (ultraHTE) platform for PROTAC synthesis, followed by direct addition of the crude reaction mixtures to cellular degradation assays without any purification. This 'direct-to-biology' (D2B) approach was validated and then exemplified in a medicinal chemistry campaign to identify novel BRD4 PROTACs. Using the D2B platform, the synthesis of 650 PROTACs was carried out in a 1536-well plate, and subsequent biological evaluation was performed by a single scientist in less than 1 month. Due to its ability to hugely accelerate the optimization of new degraders, we anticipate our platform will transform the synthesis and testing of PROTACs.

Ultra-high-throughput mapping of the chemical space of asymmetric catalysis enables accelerated reaction discovery

The discovery of highly enantioselective catalysts and elucidating their generality face great challenges due to the complex multidimensional chemical space of asymmetric catalysis and inefficient screening methods. Here, we develop a general strategy for ultra-high-throughput mapping of the chemical space of asymmetric catalysis by escaping the time-consuming chiral chromatography separation. The ultrafast ( ~ 1000 reactions/day) and accurate (median error < ±1%) analysis of enantiomeric excess are achieved through the ion mobility-mass spectrometry combines with the diastereoisomerization strategy. A workflow for accelerated asymmetric reaction screening is established and verified by mapping the large-scale chemical space of more than 1600 reactions of α-asymmetric alkylation of aldehyde with organocatalysis and photocatalysis. Importantly, a class of high-enantioselectivity primary amine organocatalysts of 1,2-diphenylethane-1,2-diamine-based sulfonamides is discovered by the accelerated screening, and the mechanism for high-selectivity is demonstrated by computational chemistry. This study provides a practical and robust solution for large-scale screening and discovery of asymmetric reactions.

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R2 of 0.712) was obtained on our in-house data. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction.

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.

MOFganic Chemistry: Challenges and Opportunities for Metal-Organic Frameworks in Synthetic Organic Chemistry

Metal-organic frameworks (MOFs) are porous, crystalline solids constructed from organic linkers and inorganic nodes that have been widely studied for applications in gas storage, chemical separations, and drug delivery. Owing to their highly modular structures and tunable pore environments, we propose that MOFs have significant untapped potential as catalysts and reagents relevant to the synthesis of next-generation therapeutics. Herein, we outline the properties of MOFs that make them promising for applications in synthetic organic chemistry, including new reactivity and selectivity, enhanced robustness, and user-friendly preparation. In addition, we outline the challenges facing the field and propose new directions to maximize the utility of MOFs for drug synthesis. This perspective aims to bring together the organic and MOF communities to develop new heterogeneous platforms capable of achieving synthetic transformations that cannot be replicated by homogeneous systems.

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.

Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit

Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.

High-Throughput Diversification of Complex Bioactive Molecules by Accelerated Synthesis in Microdroplets

Late-stage diversification of drug molecules is an important strategy in drug discovery that can be facilitated by reaction screening using high-throughput experimentation. Here we present a rapid method for functionalizing bioactive molecules based on accelerated reactions in microdroplets. Reaction mixtures are nebulized at throughputs better than 1 reaction/second and the accelerated reactions occurring in the microdroplets are followed by desorption electrospray ionization mass spectrometry (DESI-MS). Because the accelerated reactions occur on the millisecond timescale, they allow an overall screening throughput of 1 Hz working at the low nanogram scale. Using this approach, an opioid agonist (PZM21) and an antagonist (naloxone) were diversified using three reactions important in medicinal chemistry: sulfur fluoride exchange (SuFEx) click reactions, imine formation reactions, and ene-type click reactions. Some 269 functionalized analogs of naloxone and PZM21 were generated and characterized by tandem mass spectrometry (MS/MS) after screening over 500 reactions.

A field guide to flow chemistry for synthetic organic chemists

Flow chemistry has unlocked a world of possibilities for the synthetic community, but the idea that it is a mysterious "black box" needs to go. In this review, we show that several of the benefits of microreactor technology can be exploited to push the boundaries in organic synthesis and to unleash unique reactivity and selectivity. By "lifting the veil" on some of the governing principles behind the observed trends, we hope that this review will serve as a useful field guide for those interested in diving into flow chemistry.

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.

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.

HTE- and AI-assisted development of DHP-catalyzed decarboxylative selenation

1,4-Dihydropyridine (DHP) derivatives play key roles in biology, but are rarely used as catalysts in synthesis. Here, we developed a DHP derivative-catalyzed decarboxylative selenation reaction that showed a broad substrate scope, with the assistance of high-throughput experimentation (HTE) and artificial intelligence (AI). The AI-based model could identify the key structural features and give accurate prediction of unseen reactions (R² = 0.89, RMSE = 9.0%, and MAE = 6.3%). Our work not only developed the catalytic applications of DHP derivatives, but also demonstrated the power of the combination of HTE and AI to advance chemical synthesis.

'Chemistry at the speed of sound': automated 1536-well nanoscale synthesis of 16 scaffolds in parallel

Screening of large and diverse libraries is the 'bread and butter' in the first phase of the discovery of novel drugs. However, maintenance and periodic renewal of high-quality large compound collections pose considerable logistic, environmental and monetary problems. Here, we exercise an alternative, the 'on-the-fly' synthesis of large and diverse libraries on a nanoscale in a highly automated fashion. For the first time, we show the feasibility of the synthesis of a large library based on 16 different chemistries in parallel on several 384-well plates using the acoustic dispensing ejection (ADE) technology platform. In contrast to combinatorial chemistry, we produced 16 scaffolds at the same time and in a sparse matrix fashion, and each compound was produced by a random combination of diverse large building blocks. The synthesis, analytics, resynthesis of selected compounds, and chemoinformatic analysis of the library are described. The advantages of the herein described automated nanoscale synthesis approach include great library diversity, absence of library storage logistics, superior economics, speed of synthesis by automation, increased safety, and hence sustainable chemistry.

Robotic Construction and Screening of Lanthipeptide Variant Libraries in Escherichia coli

Lanthipeptides are a major class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by thioether cross-links called lanthionine (Lan) and methyllanthionine (MeLan). Previously, we developed a method to produce mature lanthipeptides in recombinant Escherichia coli, but manual steps hinder large-scale analogue screening. Here we devised an automated workflow for creating and screening variant libraries of haloduracin, a two-component class II lanthipeptide. An integrated work cell of a synthetic biology foundry was programmed to robotically execute DNA library construction, host transformation, peptide production, mass spectrometry analysis, and activity screening by agar diffusion assay. For recombinantly produced Halα peptides, the sequence-activity relationship of 380 single-residue variants and >1300 triple-residue combinatorial variants were rapidly analyzed in microplates within weeks. The peptide expression levels in E. coli were also visualized via robotic creation and analysis of GFP-lanthipeptide fusions for select peptide mutants. Following shake-flask fermentation and purification, one Halα mutant was confirmed with enhanced specific antimicrobial activity relative to the wild-type peptide. Overall, this approach may be generally applicable for the high-throughput characterization and engineering of RiPP natural products.

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit how much data can be obtained from experiments. This suggests the use of schemes for data collection such as active learning, which identifies the data points of highest impact for model accuracy, and which has been used in recent studies with success. However, little has been done to explore the robustness of the methods predicting reaction yield when used together with active learning to reduce the amount of experimental data needed for training. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined AUROC faster than random sampling on both datasets. Analysis of feature importance of the trained machine learning models suggests active learning had a larger influence on the model accuracy when only a few features were important for the model prediction.

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling

General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging and consequential problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches. This study provides a practical road map for solving multidimensional chemical optimization problems with large search spaces.

Innovations and Inventions: Why Was the Ugi Reaction Discovered Only 37 Years after the Passerini Reaction?

This year represents the 100th anniversary of the discovery of the Passerini three-component reaction. The related Ugi four-compound reaction was discovered 37 years after the Passerini reaction. Undoubtedly, both reactions are very important multicomponent reactions but the Ugi reactions outperform the Passerini reactions in terms of combinatorial space according to the equation x^y [x is the number of building blocks per component, and y is the order of the multicomponent reaction (for Passerini, y = 3; for Ugi, y = 4)]. In this work, a historical but contemporary perspective of the discoveries and innovations of the two reactions is given. From a bird's eye view and in a more general sense, the discovery of novel reactions is discussed and how it relates to inventions and innovations.

Development of copper-catalyzed deaminative esterification using high-throughput experimentation

Repurposing of amine and carboxylic acid building blocks provides an enormous opportunity to expand the accessible chemical space, because amine and acid feedstocks are typically low cost and available in high diversity. Herein, we report a copper-catalyzed deaminative esterification based on C-N activation of aryl amines via diazonium salt formation. The reaction was specifically designed to complement the popular amide coupling reaction. A chemoinformatic analysis of commercial building blocks demonstrates that by utilizing aryl amines, our method nearly doubles the available esterification chemical space compared to classic Fischer esterification with phenols. High-throughput experimentation in microliter reaction droplets was used to develop the reaction, along with classic scope studies, both of which demonstrated robust performance against hundreds of substrate pairs. Furthermore, we have demonstrated that this new esterification is suitable for late-stage diversification and for building-block repurposing to expand chemical space.

Directed evolution of a cyclodipeptide synthase with new activities via label-free mass spectrometric screening

Directed evolution is a powerful approach to engineer enzymes via iterative creation and screening of variant libraries. However, assay development for high-throughput mutant screening remains challenging, particularly for new catalytic activities. Mass spectrometry (MS) analysis is label-free and well suited for untargeted discovery of new enzyme products but is traditionally limited by slow speed. Here we report an automated workflow for directed evolution of new enzymatic activities via high-throughput library creation and label-free MS screening. For a proof of concept, we chose to engineer a cyclodipeptide synthase (CDPS) that synthesizes diketopiperazine (DKP) compounds with therapeutic potential. In recombinant Escherichia coli, site-saturation mutagenesis (SSM) and error-prone PCR (epPCR) libraries expressing CDPS mutants were automatically created and cultivated on an integrated work cell. Culture supernatants were then robotically processed for matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS analysis at a rate of 5 s per sample. The resulting mass spectral data were processed via custom computational algorithms, which performed a multivariant analysis of 108 theoretical mass-to-charge (m/z) values of 190 possible DKP molecules within a mass window of 115–373 Da. An F186L CDPS mutant was isolated to produce cyclo(l-Phe–l-Val), which is undetectable in the product profile of the wild-type enzyme. This robotic, label-free MS screening approach may be generally applicable to engineering other enzymes with new activities in high throughput.

A robotic workflow for directed evolution of new enzymatic activities via high-throughput library creation and label-free MS screening.

Homogeneous Organic Electron Donors in Nickel-Catalyzed Reductive Transformations

Many contemporary organic transformations, such as Ni-catalyzed cross-electrophile coupling (XEC), require a reductant. Typically, heterogeneous reductants, such as Zn0 or Mn0, are used as the electron source in these reactions. Although heterogeneous reductants are highly practical for preparative-scale batch reactions, they can lead to complications in performing reactions on process scale and are not easily compatible with modern applications, such as flow chemistry. In principle, homogeneous organic reductants can address some of the challenges associated with heterogeneous reductants and also provide greater control of the reductant strength, which can lead to new reactivity. Nevertheless, homogeneous organic reductants have rarely been used in XEC. In this Perspective, we summarize recent progress in the use of homogeneous organic electron donors in Ni-catalyzed XEC and related reactions, discuss potential synthetic and mechanistic benefits, describe the limitations that inhibit their implementation, and outline challenges that need to be solved in order for homogeneous organic reductants to be widely utilized in synthetic chemistry. Although our focus is on XEC, our discussion of the strengths and weaknesses of different methods for introducing electrons is general to other reductive transformations.

Predicting reaction conditions from limited data through active transfer learning

Transfer and active learning have the potential to accelerate the development of new chemical reactions, using prior data and new experiments to inform models that adapt to the target area of interest. This article shows how specifically tuned machine learning models, based on random forest classifiers, can expand the applicability of Pd-catalyzed cross-coupling reactions to types of nucleophiles unknown to the model. First, model transfer is shown to be effective when reaction mechanisms and substrates are closely related, even when models are trained on relatively small numbers of data points. Then, a model simplification scheme is tested and found to provide comparative predictivity on reactions of new nucleophiles that include unseen reagent combinations. Lastly, for a challenging target where model transfer only provides a modest benefit over random selection, an active transfer learning strategy is introduced to improve model predictions. Simple models, composed of a small number of decision trees with limited depths, are crucial for securing generalizability, interpretability, and performance of active transfer learning.

A Full Scan Data Review Tool to Match the Speed of Acoustic Ejection Mass Spectrometry

Acoustic ejection mass spectrometry (AEMS) has recently emerged as the premier ultrahigh-throughput mass spectrometric methodology for drug discovery and related fields. The ultrahigh analytical speed (~1 s/sample) of AEMS has significantly enhanced the efficiency of many high throughput applications. As a result, a data processing and reviewing tool with a matching speed is in high demand for the large amount of data generated, especially for applications such as quality control (QC) of compound collections and high throughput chemistry, where full-scan MS data required convoluted subsequent peak extraction and evaluation. In this study, we demonstrated the feasibility of a tool developed specifically for this purpose. The process using the tool involved automated splitting of the full scan data to correlate well positions with each signal peak, extraction of expected mass traces, and subsequent peak integration. Data evaluation based on verification rules, such as detected mass accuracy, isotopic pattern, and signal-to-noise ratio (S/N), enabled a comprehensive assessment of sample quality that was complemented by visualization in the form of a plate heat map generated from the selected rules. The tool demonstrated fast and straightforward data review and reporting and, more importantly, at a matching speed of sample analysis by acoustic ejection mass spectrometry. The choice of data processing and storage over the cloud further facilitated results sharing among data users.

Trapping-Enrichment Multi-dimensional Liquid Chromatography with On-Line Deuterated Solvent Exchange for Streamlined Structure Elucidation at the Microgram Scale

At the forefront of chemistry and biology research, development timelines are fast-paced and large quantities of pure targets are rarely available. Herein, we introduce a new framework, which is built upon an automated, online trapping-enrichment multi-dimensional liquid chromatography platform (TE-Dt-mDLC) that enables: 1) highly efficient separation of complex mixtures in a first dimension (¹ D-UV); 2) automated peak trapping-enrichment and buffer removal achieved through a sequence of H₂ O and D₂ O washes using an independent pump setup; and 3) a second dimension separation (² D-UV-MS) with fully deuterated mobile phases and fraction collection to minimize protic residues for immediate NMR analysis while bypassing tedious drying processes and minimizing analyte degradation. Diverse examples of target isolation and characterization from organic synthesis and natural product chemistry laboratories are illustrated, demonstrating recoveries above 90 % using as little as a few micrograms of material.

The case for data science in experimental chemistry: examples and recommendations

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

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.

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

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

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.

High-Throughput Nano-Electrostatic-Spray Ionization/Photoreaction Mass Spectrometric Platform for the Discovery of Visible-Light-Activated Photocatalytic Reactions in the Picomole Scale

Visible-light-activated photocatalysis has emerged as a green and powerful tool for the synthesis of various organic compounds under mild conditions. However, the expeditious discovery of novel photocatalysts and synthetic pathways remains challenging. Here, we developed a bifunctional platform that enabled the high-throughput discovery and optimization of new photochemical reactions down to the picomole scale. This platform was designed based on a contactless nano-electrostatic-spray ionization technique, which allows synchronized photoreactions and high-throughput in situ mass spectrometric analysis with a near-100% duty cycle. Using this platform, we realized the rapid screening of photocatalytic reactions in ambient conditions with a high speed of less than 1.5 min/reaction using picomolar materials. The versatility was validated by multiple visible-light-induced photocatalytic reactions, especially the discovery of aerobic C-H thiolation with low-cost organic photocatalysts without any other additives. This study provided a new paradigm for the integration of ambient ionization techniques and new insights into photocatalytic reaction screening, which will have broad applications in the development of new visible-light-promoted reactions.

Automated High-Throughput System Combining Small-Scale Synthesis with Bioassays and Reaction Screening

The Purdue Make It system is a unique automated platform capable of small-scale in situ synthesis, screening small-molecule reactions, and performing direct label-free bioassays. The platform is based on desorption electrospray ionization (DESI), an ambient ionization method that allows for minimal sample workup and is capable of accelerating reactions in secondary droplets, thus conferring unique advantages compared with other high-throughput screening technologies. By combining DESI with liquid handling robotics, the system achieves throughputs of more than 1 sample/s, handling up to 6144 samples in a single run. As little as 100 fmol/spot of analyte is required to perform both initial analysis by mass spectrometry (MS) and further MSⁿ structural characterization. The data obtained are processed using custom software so that results are easily visualized as interactive heatmaps of reaction plates based on the peak intensities of m/z values of interest. In this paper, we review the system's capabilities as described in previous publications and demonstrate its utilization in two new high-throughput campaigns: (1) the screening of 188 unique combinatorial reactions (24 reaction types, 188 unique reaction mixtures) to determine reactivity trends and (2) label-free studies of the nicotinamide N-methyltransferase enzyme directly from the bioassay buffer. The system's versatility holds promise for several future directions, including the collection of secondary droplets containing the products from successful reaction screening measurements, the development of machine learning algorithms using data collected from compound library screening, and the adaption of a variety of relevant bioassays to high-throughput MS.