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.

Light-Driven, Dynamic Assembly of Micron-To-Centimeter Parts, Micromachines and Microbot Swarms

This work describes light-driven assembly of dynamic formations and functional particle swarms controlled by appropriately programmed light patterns. The system capitalizes on the use of a fluidic bed whose low thermal conductivity assures that light-generated heat remains "localized" and sets strong convective flows in the immediate vicinity of the particles being irradiated. In this way, even low-power laser light or light from a desktop slide projector can be used to organize dynamic formations of objects spanning four orders of magnitude in size (from microns to centimeters) and over nine orders of magnitude in terms of mass. These dynamic assemblies include open-lattice structures with individual particles performing intricate translational and/or rotational motions, density-gradient particle arrays, nested architectures of mechanical components (e.g., planetary gears), or swarms of light-actuated microbots controlling assembly of other objects.

Delocalized, asynchronous, closed-loop discovery of organic laser emitters

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.

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.

Late-Stage Saturation of Drug Molecules

The available methods of chemical synthesis have arguably contributed to the prevalence of aromatic rings, such as benzene, toluene, xylene, or pyridine, in modern pharmaceuticals. Many such sp2-carbon-rich fragments are now easy to synthesize using high-quality cross-coupling reactions that click together an ever-expanding menu of commercially available building blocks, but the products are flat and lipophilic, decreasing their odds of becoming marketed drugs. Converting flat aromatic molecules into saturated analogues with a higher fraction of sp3 carbons could improve their medicinal properties and facilitate the invention of safe, efficacious, metabolically stable, and soluble medicines. In this study, we show that aromatic and heteroaromatic drugs can be readily saturated under exceptionally mild rhodium-catalyzed hydrogenation, acid-mediated reduction, or photocatalyzed-hydrogenation conditions, converting sp2 carbon atoms into sp3 carbon atoms and leading to saturated molecules with improved medicinal properties. These methods are productive in diverse pockets of chemical space, producing complex saturated pharmaceuticals bearing a variety of functional groups and three-dimensional architectures. The rhodium-catalyzed method tolerates traces of dimethyl sulfoxide (DMSO) or water, meaning that pharmaceutical compound collections, which are typically stored in wet DMSO, can finally be reformatted for use as substrates for chemical synthesis. This latter application is demonstrated through the late-stage saturation (LSS) of 768 complex and densely functionalized small-molecule drugs.

Multicomponent Synthesis of α-Branched Amines via a Zinc-Mediated Carbonyl Alkylative Amination Reaction

Methods for the synthesis of α-branched alkylamines are important due to their ubiquity in biologically active molecules. Despite the development of many methods for amine preparation, C(sp3)-rich nitrogen-containing compounds continue to pose challenges for synthesis. While carbonyl reductive amination (CRA) between ketones and alkylamines is the cornerstone method for α-branched alkylamine synthesis, it is sometimes limited by the sterically demanding condensation step between dialkyl ketones and amines and the more restricted availability of ketones compared to aldehydes. We recently reported a "higher-order" variant of this transformation, carbonyl alkylative amination (CAA), which utilized a halogen atom transfer (XAT)-mediated radical mechanism, enabling the streamlined synthesis of complex α-branched alkylamines. Despite the efficacy of this visible-light-driven approach, it displayed scalability issues, and competitive reductive amination was a problem for certain substrate classes, limiting applicability. Here, we report a change in the reaction regime that expands the CAA platform through the realization of an extremely broad zinc-mediated CAA reaction. This new strategy enabled elimination of competitive CRA, simplified purification, and improved reaction scope. Furthermore, this new reaction harnessed carboxylic acid derivatives as alkyl donors and facilitated the synthesis of α-trialkyl tertiary amines, which cannot be accessed via CRA. This Zn-mediated CAA reaction can be carried out at a variety of scales, from a 10 μmol setup in microtiter plates enabling high-throughput experimentation, to the gram-scale synthesis of medicinally-relevant compounds. We believe that this transformation enables robust, efficient, and economical access to α-branched alkylamines and provides a viable alternative to the current benchmark methods.

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

Recent advances and applications in high-throughput continuous flow

High-throughput continuous flow technology has emerged as a revolutionary approach in chemical synthesis, offering accelerated experimentation and improved efficiency. With the aid of process analytical technology and automation, this system not only enables rapid optimisation of reaction conditions at the millimole to the picomole scale, but also facilitates automated scale-up synthesis. It can even achieve the self-planning and self-synthesis of small drug molecules with artificial intelligence incorporated in the system. The versatility of the system is highlighted by its compatibility with both electrochemistry and photochemistry, and its significant applications in organic synthesis and drug discovery. This highlight summarises its recent developments and applications, emphasising its significant impact on advancing research across multiple disciplines.

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.

At the Speed of Light: The Systematic Implementation of Photoredox Cross-Coupling Reactions for Medicinal Chemistry Research

The adoption of new and emerging techniques in organic synthesis is essential to promote innovation in drug discovery. In this Perspective, we detail the strategy we used for the systematic deployment of photoredox-mediated, metal-catalyzed cross-coupling reactions in AbbVie's medicinal chemistry organization, focusing on topics such as assessment, evaluation, implementation, and accessibility. The comprehensive evaluation of photoredox reaction setups and published methods will be discussed, along with internal efforts to build expertise and photoredox high-throughput experimentation capabilities. We also highlight AbbVie's academic-industry collaborations in this field that have been leveraged to develop new synthetic strategies, along with discussing the internal adoption of photoredox cross-coupling reactions. The work described herein has culminated in robust photocatalysis and cross-coupling capabilities which are viewed as key platforms for medicinal chemistry research at AbbVie.

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.

Visible-light-mediated direct C3 alkylation of quinoxalin-2(1H)-ones using alkanes

Due to the high C-H bond dissociation energy of alkanes, the utilization of alkanes as alkyl radical precursors for C-H functionalization of heteroarenes is synthetically captivating but practically challenging, especially under metal- and photocatalyst-free conditions. We report herein a mild and practical visible-light-mediated method for C-H alkylation of quinoxalin-2(1H)-ones using trifluoroacetic acid as a hydrogen atom transfer reagent and air as an oxidant. This mild protocol was performed under metal- and photocatalyst-free circumstances and presented good functional-group tolerance as well as a broad substrate scope.

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery

We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.

Exploring the combinatorial explosion of amine-acid reaction space via graph editing

Amines and carboxylic acids are abundant chemical feedstocks that are nearly exclusively united via the amide coupling reaction. The disproportionate use of the amide coupling leaves a large section of unexplored reaction space between amines and acids: two of the most common chemical building blocks. Herein we conduct a thorough exploration of amine-acid reaction space via systematic enumeration of reactions involving a simple amine-carboxylic acid pair. This approach to chemical space exploration investigates the coarse and fine modulation of physicochemical properties and molecular shapes. With the invention of reaction methods becoming increasingly automated and bringing conceptual reactions into reality, our map provides an entirely new axis of chemical space exploration for rational property design.

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

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

Accelerating Pharmaceutical Process Development with an Acoustic Droplet Ejection-Multiple Reaction Monitoring-Mass Spectrometry Workflow

Fast-paced pharmaceutical process developments (e.g., high-throughput experimentation, directed evolution, and machine learning) involve the introduction of fast, sensitive, and accurate analytical assays using limited sample volumes. In recent years, acoustic droplet ejection (ADE) coupled with an open port interface has been invented as a sampling technology for mass spectrometry, providing high-throughput nanoliter analytical measurements directly from the standard microplates. Herein, we introduce an ADE-multiple reaction monitoring-mass spectrometry (ADE-MRM-MS) workflow to accelerate pharmaceutical process research and development (PR&D). This systematic workflow outlines the selection of MRM transitions and optimization of assay parameters in a data-driven manner using rapid measurements (1 sample/s). The synergy between ADE sampling and MRM analysis enables analytical assays with excellent sensitivity, selectivity, and speed for PR&D reaction screenings. This workflow was utilized to develop new ADE-MRM-MS assays guiding a variety of industrial processes, including (1) screening of Ni-based catalysts for C-N cross-coupling reaction at 1 Hz and (2) high-throughput regioisomer analysis-enabled enzyme library screening for peptide ligation reaction. ADE-MRM-MS assays were demonstrated to deliver accurate results that are comparable to conventional liquid chromatography (LC) experiments while providing >100-fold throughput enhancement.

Predicting success in Cu-catalyzed C-N coupling reactions using data science

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.

Direct Heterocycle C-H Alkenylation via Dual Catalysis Using a Palladacycle Precatalyst: Multifactor Optimization and Scope Exploration Enabled by High-Throughput Experimentation

One of the major challenges in developing catalytic methods for C-C bond formation is the identification of generally applicable reaction conditions, particularly if multiple substrate structural classes are involved. Pd-catalyzed direct arylation reactions are powerful transformations that enable direct functionalization of C-H bonds; however, the corresponding direct alkenylation reactions, using vinyl (pseudo) halide electrophiles, are less well developed. Inspired by process development efforts toward GSK3368715, an investigational active pharmaceutical ingredient, we report that a Pd(II) palladacycle derived from tri-tert-butylphosphine and Pd(OAc)2 is an effective single-component precatalyst for a variety of direct alkenylation reactions. High-throughput experimentation identified optimal solvent/base combinations for a variety of HetAr-H substrate classes undergoing C-H activation without the need for cocatalysts or stoichiometric silver bases (e.g., Ag2CO3). We propose this reaction proceeds via a dual cooperative catalytic mechanism, where in situ-generated Pd(0) supports a canonical Pd(0)/(II) cross-coupling cycle and the palladacycle effects C-H activation via CMD in a redox-neutral cycle. In all, 192 substrate combinations were tested with a hit rate of approximately 40% and 24 isolated examples. Importantly, this method was applied to prepare a key intermediate in the synthesis of GSK3368715 on multigram scale.

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

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

Enabling synthesis in fragment-based drug discovery (FBDD): microscale high-throughput optimisation of the medicinal chemist's toolbox reactions

Miniaturised high-throughput experimentation (HTE) is widely employed in industrial and academic laboratories for rapid reaction optimisation using material-limited, multifactorial reaction condition screening. In fragment-based drug discovery (FBDD), common toolbox reactions such as the Suzuki-Miyaura and Buchwald-Hartwig cross couplings can be hampered by the fragment's intrinsic heteroatom-rich pharmacophore which is required for ligand-protein binding. At Astex, we are using microscale HTE to speed up reaction optimisation and prevent target down-prioritisation. By identifying catalyst/base/solvent combinations which tolerate unprotected heteroatoms we can rapidly optimise key cross-couplings and expedite route design by avoiding superfluous protecting group manipulations. However, HTE requires extensive upfront training, and this modern automated synthesis technique largely differs to the way organic chemists are traditionally trained. To make HTE accessible to all our synthetic chemists we have developed a semi-automated workflow enabled by pre-made 96-well screening kits, rapid analytical methods and in-house software development, which is empowering chemists at Astex to run HTE screens independently with minimal training.

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.

High-Throughput Liquid Chromatographic Analysis Using a Segmented Flow Injector with a 1 s Cycle Time

High-throughput screening (HTS) workflows are revolutionizing many fields, including drug discovery, reaction discovery and optimization, diagnostics, sensing, and enzyme engineering. Liquid chromatography (LC) is commonly deployed during HTS to reduce matrix effects, distinguish isomers, and preconcentrate prior to detection, but LC separation time often limits throughput. Although subsecond LC separations have been demonstrated, they are rarely utilized during HTS due to limitations associated with the speed of common autosamplers. In this work, these limits are overcome by utilizing droplet microfluidics for sample introduction. In the method, a train of samples segmented by air are continuously pumped into the inlet of an LC injection valve that is actuated once each sample fills the sample loop. Coupled with 2.1 mm diameter × 5 mm long columns packed with 2.7 μm superficially porous C18 particles operated at 5 mL/min, the injector enabled separation of 3 components at 1 s/sample and analysis of a 96-well plate in 1.6 min with <2% peak area relative standard deviation. Analyte-dependent carryover was minimized by including wash droplets composed of organic solvent in between sample droplets. High-throughput LC coupled with mass spectrometric detection using the segmented flow injector was applied to a screen of inhibitors of a cytochrome P450-catalyzed hydroxylation reaction. Measurements of the reaction substrate and product concentrations made using fast LC with the segmented flow injector correlated well with measurements made using a more conventional, 3 min LC method. These results demonstrate the potential for droplet microfluidics to be used for sample introduction during high-throughput LC analysis.

Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening

Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

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 machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C-N couplings

Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)-catalyzed carbon-nitrogen (C-N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C-N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows.

Parallel multi-droplet platform for reaction kinetics and optimization

We present an automated droplet reactor platform possessing parallel reactor channels and a scheduling algorithm that orchestrates all of the parallel hardware operations and ensures droplet integrity as well as overall efficiency. We design and incorporate all of the necessary hardware and software to enable the platform to be used to study both thermal and photochemical reactions. We incorporate a Bayesian optimization algorithm into the control software to enable reaction optimization over both categorical and continuous variables. We demonstrate the capabilities of both the preliminary single-channel and parallelized versions of the platform using a series of model thermal and photochemical reactions. We conduct a series of reaction optimization campaigns and demonstrate rapid acquisition of the data necessary to determine reaction kinetics. The platform is flexible in terms of use case: it can be used either to investigate reaction kinetics or to perform reaction optimization over a wide range of chemical domains.

Translating Planar Heterocycles into Three-Dimensional Analogs by Photoinduced Hydrocarboxylation

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

Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approaches

High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules provide a possibility for quick material discovery and machine learning based on experimental data. However, the diversity of the molecular structure leads to the complexity of molecular properties, such as solubility, polarity, and crystallinity, which poses great challenges to solution processing and purification. Here, we first report an integrated system for the high-throughput synthesis, purification, and characterization of molecules with a large variety. Based on the principle "Like dissolves like," we combine theoretical calculations and a robotic platform to accelerate the purification of those molecules. With this platform, a material library containing 125 molecules and their optical-electronic properties was built within a timeframe of weeks. More importantly, the high repeatability of recrystallization we design is a reliable approach to further upgrading and industrial production.

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.

Advances in coupling droplet microfluidics to mass spectrometry

Droplet microfluidics enables development of workflows with low sample consumption and high throughput. Fluorescence-based assays are most used with droplet microfluidics; however, the requirement of a fluorescent reporter restricts applicability of this approach. The coupling of droplets to mass spectrometry (MS) has enabled selective assays on complex mixtures to broaden the analyte scope. Droplet microfluidics has been interfaced to MS via electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI). The works reviewed herein outline the development of this nascent field as well as initial exploration of its application in biotechnology and bioanalysis, including synthetic biology, reaction development, and in vivo sensing.

Recent Developments in Mass Spectrometry to Support Next-Generation Synthesis and Screening

The complexity of new therapeutics continues to increase and the timeline for the discovery of these therapeutics continues to shrink. This creates demand for new analytical techniques to facilitate quicker discovery and development of novel drugs. Mass spectrometry is one of the most prolific analytical techniques that has been applied across the entire drug discovery pipeline. New mass spectrometers and the associated methods for sampling have been introduced at a rate that keeps pace with new chemistries, therapeutic types, and screening practices used by modern drug hunters. This microperspective covers application and implementation of new mass spectrometry workflows that enable current and future efforts in screening and synthesis for drug discovery.

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

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.

Ni-catalyzed mild hydrogenolysis and oxidations of C-O bonds via carbonate redox tags

Oxygenated molecules are omnipresent in natural as well as artificial settings making the redox transformation of the present C-O bonds a central tool for their processing. However, the required (super)stoichiometric redox agents which traditionally include highly reactive and hazardous reagents pose multiple practical challenges including process safety hazards or special waste management requirements. Here, we report a mild Ni-catalyzed fragmentation strategy based on carbonate redox tags for redox transformations of oxygenated hydrocarbons in the absence of any external redox equivalents or other additives. The purely catalytic process enables the hydrogenolysis of strong C(sp²)-O bonds including that of enol carbonates as well as the catalytic oxidation of C-O bonds under mild conditions down to room temperature. Additionally, we investigated the underlying mechanism and showcased the benefits of carbonate redox tags in multiple applications. More broadly, the work herein demonstrates the potential of redox tags for organic synthesis.

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.

ChemBeads-Enabled Photoredox High-Throughput Experimentation Platform to Improve C(sp2)-C(sp3) Decarboxylative Couplings

Enthusiasm surrounding nickel/photoredox C(sp2)-C(sp3) cross-couplings is very high; however, these methods are sometimes challenged by complex drug-like substrates in discovery chemistry. In our hands this has been especially true of the decarboxylative coupling, which has lagged behind other photoredox couplings in internal adoption and success. Herein, the development of a photoredox high-throughput experimentation platform to optimize challenging C(sp2)-C(sp3) decarboxylative couplings is described. Chemical-coated glass beads (ChemBeads) and a novel parallel bead dispenser are used to expedite the high-throughput experimentation process and identify improved coupling conditions. In this report, photoredox high-throughput experimentation is utilized to dramatically improve low-yielding decarboxylative C(sp2)-C(sp3) couplings, and libraries, using conditions not previously identified in the literature.

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.

Diversifying Amino Acids and Peptides via Deaminative Reductive Cross-Couplings Leveraging High-Throughput Experimentation

A deaminative reductive coupling of amino acid pyridinium salts with aryl bromides has been developed to enable efficient synthesis of noncanonical amino acids and diversification of peptides. This method transforms natural, commercially available lysine, ornithine, diaminobutanoic acid, and diaminopropanoic acid to aryl alanines and homologated derivatives with varying chain lengths. Attractive features include ability to transverse scales, tolerance of pharma-relevant (hetero)aryls and biorthogonal functional groups, and the applicability beyond monomeric amino acids to short and macrocyclic peptide substrates. The success of this work relied on high-throughput experimentation to identify complementary reaction conditions that proved critical for achieving the coupling of a broad scope of aryl bromides with a range of amino acid and peptide substrates including macrocyclic peptides.

Nanoliter Scale Parallel Liquid-Liquid Extraction for High-Throughput Purification on a Droplet Microarray

In the current drug discovery process, the synthesis of compound libraries is separated from biological screenings both conceptually and technologically. One of the reasons is that parallel on-chip high-throughput purification of synthesized compounds is still a major challenge. Here, on-chip miniaturized high-throughput liquid-liquid extraction in volumes down to 150 nL with efficiency comparable to or better than large-scale extraction utilizing separation funnels is demonstrated. The method is based on automated and programmable merging of arrays of aqueous nanoliter droplets with organic droplets. Multi-step extraction performed simultaneously or with changing conditions as well as handling of femtomoles of compounds are demonstrated. In addition, the extraction efficiency is analyzed with a fast optical readout as well as matrix-assisted laser desorption ionization-mass spectrometry on-chip detection. The new massively parallel and miniaturized purification method adds another important tool to the chemBIOS concept combining chemical combinatorial synthesis with biological screenings on the same miniaturized droplet microarray platform, which will be essential to accelerate drug discovery.

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening

Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors' hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC-DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA's data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA's peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.

Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials

Artificial intelligence technology shows the advantages of improving efficiency, reducing costs, shortening time, reducing the number of staff on site and achieving precise operations, making impressive research progress in the fields of drug discovery and development, but there are few reports on application in energetic materials. This paper addresses the high safety risks in the current nitrification process of energetic materials, comprehensively analyses and summarizes the main safety risks and their control elements in the nitrification process, proposes possibilities and suggestions for using artificial intelligence technology to enhance the "essential safety" of the nitrification process in energetic materials, reviews the research progress of artificial intelligence in the field of drug synthesis, looks forward to the application prospects of artificial intelligence technology in the nitrification of energetic materials and provides support and guidance for the safe processing of nitrification in the propellants and explosives industry.

AROPS: A Framework of Automated Reaction Optimization with Parallelized Scheduling

With the development of automated experimental platforms and optimization algorithms, chemists can easily optimize chemical reactions in an automated and high-throughput fashion. However, the modules in existing automated experimental platforms are operated in a linear fashion without orchestrating with the optimization algorithm, thus leaving room for further efficiency improvement. Here, we introduced a framework of automated reaction optimization with parallelized scheduling (AROPS) to realize the integration of the optimization algorithm and module scheduling. AROPS relies on a customized Bayesian optimizer to solve multi-reactor/analyzer reaction optimization problems with three different scheduling modes to arrange tasks for various experimental modules. In addition, a mechanism based on probability of improvement (PI) for discarding unpromising ongoing experiments was developed to facilitate freeing up valuable experimental resources in parallelized optimization. We tested the performance of AROPS using a hardware emulator on three representative benchmark reactions encountered in organic synthesis, illustrating that AROPS can trade off optimization time and cost according to the chemists' preference.

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.

End-to-End Automated Synthesis of C(sp3)-Enriched Drug-like Molecules via Negishi Coupling and Novel, Automated Liquid-Liquid Extraction

Herein, we report an end-to-end process including synthesis, work-up, purification, and post-purification with minimal human intervention using Negishi coupling as a key transformation to increase Fsp3 in bioactive molecules. The main advantages of this protocol are twofold. First, the automated sequential generation of organozinc reagents from readily available alkyl halides offers a large diversity of alkyl groups to functionalize (hetero)aryl halide scaffolds via Pd-catalyzed Negishi coupling in continuous flow. Second, a fully automated liquid-liquid extraction has been developed and successfully applied for unattended operations. The workflow was completed with mass-triggered preparative high-performance liquid chromatography HPLC, providing an efficient production line of compounds with enriched sp3 character and better drug-like properties. The modular nature allows a smooth adaptation to a wide variety of synthetic methods and protocols and makes it applicable to any medchem laboratory.