Advancing Organic Chemistry Using High‐Throughput Experimentation

Abstract

High‐throughput experimentation (HTE), the miniaturization and parallelization of reactions, is a valuable tool for accelerating diverse compound library generation, optimizing reaction conditions, and enabling data collection for machine learning (ML) applications. When applied to organic synthesis and methodology, HTE still poses various challenges due to the diverse workflows and reagents required, motivating advancements in reaction design, execution, analysis, and data management. To address these limitations, cutting‐edge technologies, automation, and artificial intelligence (AI) have been implemented to standardize protocols, enhance reproducibility, and improve efficiency. Additionally, strategies to reduce bias and promote serendipitous discoveries have further strengthened HTE's impact. This review highlights recent advances at every stage of the HTE workflow, including the development of customized workflows, diverse analysis, and improved data management practices for greater accessibility and shareability. Furthermore, we examine the current state of the field, outstanding challenges, and future directions toward transforming HTE into a fully integrated, flexible, and democratized platform that drives innovation in organic synthesis.

This review outlines major advances in the design, execution, analysis, and data management phases of high‐throughput experimentation (HTE). The limitations and potential opportunities of applying modern HTE to organic synthesis are highlighted.

1. High‐Throughput Experimentation in Organic Chemistry

High‐throughput experimentation (HTE) is a method of scientific inquiry that facilitates the evaluation of miniaturized reactions in parallel. This approach advances the assessment of a range of experiments, allowing the exploration of multiple factors simultaneously in contrast to the traditional one variable at a time (OVAT) method. The foundational principles of HTE have long been established from high‐throughput screening (HTS), which has been discussed thoroughly in prior reports.^{[ 1} , ² , ³ , ⁴ , ⁵ , ⁶ , ⁷ , ^{8 ]} When applied to organic chemistry, HTE enables accelerated data generation, providing a wealth of information that can be leveraged to access target molecules, optimize reactions, and inform reaction discovery while enhancing cost and material efficiency. Additionally, HTE has proven effective in collecting robust and comprehensive data for machine learning (ML) algorithms—programs that learn and improve from data—that are more accurate and reliable.^{[ 9 ]}

Recent state‐of‐the‐art technologies, including automated instrumentation and data analysis tools, have expanded the capabilities of HTE.^{[ 10} , ¹¹ , ¹² , ¹³ , ¹⁴ , ¹⁵ , ¹⁶ , ^{17 ]} The introduction of ultra‐HTE, which allows for testing 1536 reactions simultaneously, has significantly accelerated data generation and broadened the ability to examine reaction chemical space. Similarly, advances in analytical techniques, such as mass spectrometry (MS), coupled with data visualization software, have enabled reaction monitoring and efficient data evaluation. However, the full potential of HTE has yet to be realized in synthetic organic chemistry through the development of practices that emphasize strategic plate design and improve the cost and flexibility of technologies that enhance the speed and reproducibility of reactions. Additionally, effective data management consistent with findable, accessible, interoperable, and reuseable (FAIR) principles will be key to establishing HTE's utility.^{[ 18} , ^{19 ]} Previous reviews on the topic of HTE have focused either on specific limitations and advances of one aspect of the HTE workflow or are written from an industrial point of view.^{[ 11} , ¹² , ¹³ , ¹⁴ , ¹⁵ , ²⁰ , ²¹ , ²² , ²³ , ²⁴ , ²⁵ , ²⁶ , ²⁷ , ²⁸ , ²⁹ , ³⁰ , ^{31 ]} This review highlights the limitations of HTE for synthetic methodology and discusses major advances, from an academic standpoint, to overcome these barriers and enhance the workflow and flexibility of HTE. Finally, we provide our perspective on outstanding challenges in the field and the opportunities that have emerged to maximize the impact of HTE on the synthetic community.

1.1. Origin and Development of Modern HTE

Modern HTE originates from well‐established HTS protocols from the 1950s that were used predominately to screen for biological activity (Figure 1a). During the mid‐1980s the term “HTE” was coined and the first solid‐phase peptide synthesis using microtiter plates was reported.^{[ 32} , ³³ , ^{34 ]} In the late 1990s, advances in automation and protocol standardization were established for HTS and the workflow that consists of running and assaying organic molecules for biological activities was drastically enhanced from 100 compounds/week (1980s) to 10000 compounds/day (1990s).^{[ 6 ]} Inspired by the rapid evaluation of large numbers of compounds, early attempts to apply HTS strategies to drug discovery in HTE gained traction. However, these efforts frequently produced compounds with poor properties or limited biological relevance.^{[ 35} , ^{36 ]} Thus, the widespread adoption of HTE for chemical synthesis was limited until successful examples of its application were demonstrated.^{[ 20 ]} Between the mid‐1990s and early 2000s, automation was repurposed for chemical synthesis and reaction development with advancement in commercial equipment that are compatible with a range of different types of chemistry and in situ reaction monitoring. This contributed to the growth of HTE use in both industrial and academic settings.^{[ 37 ]} Notably, foundational advances in implementing HTE for material synthesis provided further conceptual and practical inspiration for its application to organic reaction development.^{[ 38} , ³⁹ , ^{40 ]}

Figure 1.

Origin and development of high‐throughput experimentation (HTE). a) Timeline depicting the development of HTE from high‐throughput screening (HTS).^{[ 17} , ³² , ⁴³ , ⁴⁸ , ⁴⁹ , ⁵⁰ , ⁵¹ , ⁵² , ^{53 ]} b) Distinct applications of HTE in chemical synthesis. c) Challenges of translation of tools from biology to synthetic methodology. High‐throughput analysis (HTA), Liquid Chromatography‐Mass Spectrometry (LC‐MS), Desorption Electrospray Ionization‐Mass Spectrometry (DESI‐MS), High‐throughput Infrared (HT‐IR) Spectroscopy, High‐throughput Nuclear Magnetic Resonance (HT‐NMR) Spectroscopy, Multiple Injections in a Single Experimental Run‐Mass Spectrometry (MISER‐MS), Multiple Injections in a Single Experimental Run High‐Performance Liquid Chromatography‐Mass Spectrometry (MISER HPLC‐MS), Machine learning (ML), Artificial intelligence (AI).

Today, HTE strategies for chemical synthesis can be broadly utilized toward different objectives depending on the research goals. One common application of HTE, particularly in medicinal chemistry, is building libraries of diverse target compounds (Figure 1b–i).^{[ 13 ]} HTE has also emerged as a powerful tool for reaction optimization where multiple variables are simultaneously varied to identify an optimal condition to achieve high yield and selectivity of a product (Figure 1b–ii).^{[ 21 ]} More recently, HTE has been applied to reaction discovery, expanding its role beyond optimization to identifying unique transformations (Figure 1b–iii). Although the discovery of new reactions through HTE remains challenging, multiple reports have aimed to overcome these barriers.^{[ 15} , ⁴¹ , ⁴² , ⁴³ , ⁴⁴ , ⁴⁵ , ^{46 ]} A major advance at the turn of the 21st century was the introduction of artificial intelligence (AI) concepts into the HTE workflow, facilitating reaction setup, data analysis, and predictive modeling (Figure 1b–iv). AI‐driven approaches leverage HTE data to not only refine conditions but also to uncover reactivity patterns by analyzing large data sets across diverse substrates, catalysts, and reagents.^{[ 9} , ²⁴ , ²⁶ , ^{47 ]} This convergence of HTE with AI has improved reaction understanding in selecting variables to screen, expanded substrate scopes, and enhanced reaction yields and selectivity. HTE can generate high‐quality and reproducible data sets (both negative and positive results) for effective training of ML algorithms. As such, HTE serves as a versatile foundation for both improving existing methodologies and pioneering chemical space exploration.^{[ 9 ]}

1.2. Limitations in HTE Adoption for Reaction Development

Despite its promising application thus far, HTE adoption in reaction development can be complex and challenging. A key difficulty arises with the need for modularity as diverse sets of reactions require flexible equipment and analytical methods. When generating molecular libraries, a single set of conditions is often sufficient, which makes the workflow and process more straightforward. However, for reaction optimization or discovery, HTE must accommodate examining multiple variables (such as solvents, catalysts, reagents, temperatures, and irradiation) that often require workup prior to analysis within the same workflow (Figure 1c).^{[ 20 ]} Additionally, adapting instrumentation used for HTS, which are designed for aqueous solutions, can be challenging due to the wide range of solvents used in organic chemistry, many of which exhibit a range of surface tensions, viscosities, and material compatibility. The air sensitivity of many reactions further requires inert atmospheres for plate set‐up and experimentation, adding to the cost and complexity of protocols.^{[ 20} , ^{37 ]} Achieving this versatility is difficult and necessitates advancements in equipment, careful reaction design, and efficient data collection and analysis. Finally, the cost of establishing and maintaining HTE infrastructure in non‐industrial settings that is staffed full‐time with expert personnel to train students remains an outstanding challenge in the field.^{[ 37 ]}

These complexities have led to barriers for the widespread adoption of HTE in the synthetic community, especially in academia. In contrast to industry, which often has access to dedicated HTE infrastructure and staff support, academic labs may struggle to obtain the required instrumentation, resources, and expertise to establish an HTE workflow. The high turn‐over of researchers in academic settings presents an additional challenge to training students on how to strategically implement HTE in their research endeavors. Thus, there remains a need for further advancements in HTE strategies, technologies, and training to make the transition to HTE less daunting. This section summarizes the major limitations typically encountered during the four main stages of HTE: experiment design, reaction execution, data analysis, and data management.

A common misconception that hinders the widespread adoption of HTE is that it is mainly serendipitous and based on random screening of reactants and reagents.^{[ 54 ]} On the contrary, similar to traditional experimentation, HTE involves rigorously testing reaction conditions based on literature precedent and formulated hypotheses.^{[ 54 ]} The strength of HTE is in the exploration of a broad chemical space by conducting experiments in parallel, providing an enhanced and more detailed understanding of a reaction or a reaction class. Enabled by automation, HTE also provides an improvement in experiment precision and reproducibility than is typically provided by manual experimentation. Although HTE allows the setup of numerous reactions at once, the number of reaction conditions that can be tested is still limited by time and resources. Thus, when approaching reaction discovery, selection of initial conditions for experiments becomes even more important in maintaining a comprehensive set of conditions and limiting selection bias.^{[ 28} , ^{55 ]} For example, reagent choices are often influenced by availability, cost, ease‐of‐handling, and prior experimental experience. Although this is important to consider, solely depending on these factors can limit exploration, leading to reduced chances of uncovering novel catalysts or unconventional reactivity.^{[ 56} , ^{57 ]}

Another crucial factor to the success of an HTE experiment is ensuring that the obtained results are reproducible and consistent across a single microtiter plate (MTP). This can be challenging due to the micro or nano scale nature of HTE. Similar to traditional experimentation, each well can experience random bias such as reagent evaporation or liquid splashing while dispensing.^{[ 58 ]} However, an additional consideration for HTE is spatial bias due to discrepancies between center and edge wells resulting in uneven stirring and temperature distribution.^{[ 59 ]} This issue becomes most pronounced in photoredox chemistry, where inconsistent light irradiation and localized overheating can significantly impact reaction outcomes.^{[ 60} , ^{61 ]} Although several advances in plate equipment and automation have provided solutions to mitigate these challenges, spatial effects remain a concern in light‐driven or thermally sensitive transformations (vide infra, Section 2.1). Thus, it is crucial to run duplicates within a plate or to employ statistical analysis to ensure reliability of the experiments. To increase efficiency and reproducibility throughout the plate, automation such as liquid handlers and solid dispensers has been increasingly integrated into the workflow of HTE especially in industrial contexts. Despite advances in automation that improved HTE execution and miniaturization, these systems often lack the flexibility and adaptability needed for distinct types of chemistry. Unlike well‐defined processes in industry, academic research includes a range of experiments with unique objectives, procedures, and desired outcomes. Therefore, it can become challenging to automate and integrate instrumentation among academic research groups.

To accommodate different reaction types within a workflow, customizing protocols for automated reaction setup or workup might be required. This poses a barrier to adopting HTE as organic chemists are not routinely trained in automation or programming, and academic institutions rarely have staff who are both experts in synthetic chemistry and in coding or engineering. Furthermore, although companies like Analytical Sales^{[ 62 ]} and Chemglass^{[ 63 ]} offer affordable options to support experimental setups, and platforms like Opentrons^{[ 64 ]} provide entry points for semi‐automated workflows, access to state‐of‐the‐art fully automated platforms remains limited for academic research groups or small start‐up companies. This is primarily due to the high cost and lack of HTE core facilities at institutions which can customize workflows with advanced HTE technologies for specific user needs.^{[ 65 ]} While occasional access to industrial HTE facilities through collaboration is possible, establishing and maintaining such infrastructure independently in an academic setting is often not economically feasible.^{[ 65 ]}

Analysis is another important challenge to overcome in adopting modern HTE. With the increase in experimental throughput, it is crucial to avoid this stage of the workflow from becoming the bottleneck in reaction development. Conventional analytical tools are unable to efficiently analyze the large number of samples and require modification to allow for high‐throughput analysis (HTA).^{[ 30 ]} Due to their rapid analysis, sensitivity, and ability to handle complex mixtures, mass‐based methods have been widely adopted for application in HTE. Although commonly utilized, this mode of analysis relies on detecting known mass fragments, rendering detection of unanticipated or novel reaction outcomes challenging, which is needed for reaction discovery applications where unforeseen outcomes are crucial. Furthermore, the determination of product yield can be tedious or unreliable due to several factors, such as the need for calibration and discrepancy in ionization efficiencies of compounds depending on their structure. Complementary to MS, nuclear magnetic resonance (NMR) spectroscopy offers advantages for structure elucidation and quantification of well‐defined compounds or simple mixtures. However, its speed of data acquisition limits its compatibility for HTE analysis.^{[ 29 ]} Although several groups have integrated benchtop NMR with HTE, the sensitivity and accessibility to these instruments can be limiting in comparison to the commonly available MS instrumentation.^{[ 66} , ⁶⁷ , ⁶⁸ , ⁶⁹ , ^{70 ]} This trade‐off between the speed and the quality of data analysis remains a major concern for the advancement of HTE. In addition, the diversity of instrumentation used and the data obtained per reaction is relatively limited. Oftentimes the reaction outcome success is defined by yield and selectivity.^{[ 1} , ^{29 ]} However, other forms of data or reaction sampling (luminescence, fluorescence, and redox potentials) that can reveal mechanistic insights or provide information on active species are not extensively employed by research groups. Additionally, tracking multivariate reactions in situ to gain knowledge of kinetics or real‐time catalyst or reagent dynamics remains technically challenging and the information gained from each experiment in HTE is yet to be maximized despite notable examples that demonstrate the value and feasibility of such approaches.^{[ 71} , ⁷² , ⁷³ , ⁷⁴ , ^{75 ]} The existence of a scattered workflow where different components and instruments are not integrated hinders the ability to harness the full potential of “data‐rich” experimentation. Therefore, a versatile analytical approach is a prerequisite to address current challenges in HTE, broadening its application and impact on discovery chemistry.

Upon the analysis phase in HTE, an extensive amount of data is generated, which requires efficient processing to compare reaction outcomes across experiments. Despite the availability of data storage and management solutions, they are often not capable of handling the scale and complexity of data generated by HTE. Consequently, software that supports data visualization or statistical analysis of large datasets are valuable. Moreover, automating these processes enhances throughput and eases interpretation. Compared to traditional synthesis or HTS, HTE produces a volume of data orders of magnitude higher. This includes, but is not limited to, equipment details, experimental parameters, sample identifiers, and data processing methods that need to be stored efficiently and retrieved easily to ensure data reproducibility and interoperability among different labs. Thus, the management, storage, and sharing of data—from experimental setup to analysis—should be addressed to ensure accessibility, enhance interoperability, and facilitate scientific dissemination. To date, however, there is a dearth of user‐friendly and cost‐effective platforms that allow storage of data in accessible formats suitable for computational processing. Results obtained from different analytical tools can further hinder the streamlining and standardization of data management protocols. In addition, standardized software for managing such data is still underdeveloped or proprietary. This makes it challenging to extract data from existing literature, which impedes its usefulness, especially for ML applications.^{[ 9} , ^{76 ]} Compounding this challenge, only few institutions possess training or expertise in both synthesis and computer science. Additionally, ML models commonly used in HTE workflows have limited interpretability which hinders their utility for extracting mechanistic information. Consequently, despite its potential, ML has yet to become commonplace in many synthetic labs.

2. Advances in HTE for Reaction Development and Discovery

2.1. Experimental Design

While the focus of HTS is to evaluate the activity of large libraries of compounds under fixed conditions, HTE explores a broad range of variables and reaction conditions. As a result, experimental design in HTE is inherently more complex. Consequently, the design of well plates is a critical first step in any HTE campaign and tends to vary depending on the overarching goal of the study. A well‐designed experiment can minimize error and maximize the potential to rapidly optimize or discover novel reactivity. One design aspect inherent to HTE is the required attention to the physical layout of the plate to alleviate experimental artifacts due to spatial variability.^{[ 59 ]} To enhance data integrity, randomizing well positions and distributing negative or positive controls across the plate are common practices.^{[ 77 ]} Additionally, one can choose to include replicates to assess the variability and increase the confidence in the results obtained.^{[ 78 ]}

For many HTE efforts, knowledge‐driven designs remain the default approach. These designs rely on chemical intuition or literature precedent which can be beneficial but inherently biases the screening process. Thus, the distribution of data that is attained tends to be confined to what has already been reported.^{[ 54} , ⁵⁵ , ⁷⁹ , ⁸⁰ , ^{81 ]} This can diminish the impact of the data obtained as it will not be representative of a larger distribution of data points and cannot be applied effectively to predictive modeling or chemistry. Acknowledging this bias and developing strategic HTE experiments with unique reaction designs are crucial to achieve desired results for efficient reaction optimization and discovery.

As the number of variables increases, heuristic designs can overlook important factors and interactions in chemical space. Moreover, comprehensive exploration of every reaction variable is both cost and labor intensive. Therefore, it becomes imperative to adopt tools and strategies that can facilitate exploration of a wide range of multidimensional parameters and capture complex relationships between them. To overcome this challenge, design of experiments (DoE), a statistical tool for planning and analyzing experimental data, can be utilized to facilitate the selection of reaction conditions.^{[ 82 ]} DoE has been implemented in traditional synthesis for reaction optimization, but recently, its application in HTE has become more prominent.^{[ 83 ]} Through the strategic selection of experiments, a broad range of reaction conditions can be tested with a minimal number of experiments (Figure 2a).^{[ 84 ]} To launch a DoE campaign, an objective for a reaction, usually optimizing yield or selectivity, should be established. This is followed by defining several quantitative factors such as concentration, reagent stoichiometry, and temperature, as well as categorical factors like solvent, catalyst, and additives. Although the initial selection of factors is not completely unbiased, DoE operates without preconceived hypotheses and is purely based on the statistical design of experiments. By randomizing experimental conditions and order, DoE minimizes bias across chemical space, systematically identifying significant parameters and their correlations.^{[ 84} , ^{85 ]} By tracking these effects, DoE can point toward underlying mechanistic pathways such as competing side reactions, catalyst speciation, and intermediate stabilization that become apparent through interactions between variables.

Figure 2.

Design of experiments (DoE) for the optimization of chemical reactions. a) Workflow of using DoE in reaction optimization. b) Two highlighted examples from GSK demonstrating the power of integrating DoE with high‐throughput experimentation (HTE). (i) DoE used to optimize access to an active pharmaceutical ingredient (API) via suppression of undesired product formation. R¹‐R³ denote proprietary substituents that have not been publicly disclosed. (ii) DoE employed to identify optimal parameter values for a photocatalytic reaction.

Over the past years, DoE has been successfully utilized for the systematic optimization and development of methods, particularly in pharmaceutical compound synthesis.^{[ 10} , ^{86 ]} An example that showcased an application of HTE paired with DoE is a study by researchers at GSK, where DoE was applied to aid in the process development of an active pharmaceutical ingredient (API) (Figure 2b–i).^{[ 87 ]} This approach identified the most significant reaction parameters and revealed trends in conditions that lead to efficient formation of an oxazolidine product using amino alcohols while suppressing unwanted byproduct. In another example by GSK in 2024, a photocatalytic method for coupling alcohols with various aryl and heteroaryl bromides to create new C–O bonds was optimized (Figure 2b–ii).^{[ 88 ]} In this study, HTE was initially employed to determine the reaction components required for productive chemistry and to optimize the values for these parameters. While it is possible to independently establish statistical DoE models in a lab, utilizing commercial software can make DoE more accessible. Currently, several software programs are available for users to plan and analyze experiments needed for reaction optimization. Commonly used software in academia and industry include JMP (SAS),^{[ 89 ]} MODDE (Sartorius),^{[ 90 ]} Design Expert (Stat‐Ease Inc.),^{[ 91 ]} Katalyst D2D (ACD/Labs),^{[ 92 ]} STAVEX (Aicos),^{[ 93 ]} and DoE Fusion Pro (SMatrixCorp.).^{[ 94 ]} While DoE has proven to be a successful tool for accelerated reaction optimization, the exploration of chemical space remains limited, especially as the number of factors increases or when relying on categorical variables.^{[ 9 ]} To overcome this challenge, advances in featurization utilizing ML have enabled the conversion of categorical variables into quantifiable descriptors using computational methods. Additionally, recent DoE software has incorporated ML strategies to enhance predictive modeling and provide adaptive and efficient experimentation.^{[ 95 ]}

In parallel to DoE and ML, various contemporary design strategies have been developed to optimize and discover novel reactions.^{[ 79 ]} In these methods, emphasis has been placed on implementing rational approaches that use statistical, algorithmic, or prescreening protocols to select reagents, catalysts, and conditions for a given reaction. These methods are centered on experimental design to provide researchers with tools to make informed choices about screening (Figure 3a). Ultimately, these strategies aim to provide a more systematic understanding of the reaction space, reducing trial and error while maximizing the likelihood of achieving desired outcomes.^{[ 79 ]}

Figure 3.

Contemporary reaction design to enhance generality of methods and uncover novel reactivity. a) Ideal screening goals to achieve reaction conditions that are general and unique. b) Multi‐substrate screening strategy to obtain optimized conditions that can be applied to a broad substrate scope. c) Multi‐dimensional screening approach to uncover new reactivity via unbiased and diverse exploration of chemical space. d) Mechanism‐based screening to inform reaction design and accelerate reaction discovery. Gas Chromatography‐Mass Spectrometry (GC‐MS), High‐throughput experimentation (HTE).

One attractive yet simple reaction design strategy focuses on ensuring the generality of a given reaction by screening multiple representative substrates for optimization instead of relying on one model substrate (Figure 3b). This provides a method for achieving optimized conditions for a reaction that are not biased toward a single structure, increasing the likelihood of broad functional group tolerance and reaction generality. A notable example was reported by the Jacobsen group for the development of a general method for an asymmetric Pictet–Spengler reaction.^{[ 96 ]} Representative substrate combinations were identified using a library of 340 potential products and applying a uniform manifold approximation and projection (UMAP) dimensionality reduction method to visualize chemical space. Multiple diverse and underexplored substrates were then selected. HTE aided in the execution of multiple reactions with different substrates and chiral catalysts, leading to a promising class of catalysts that offered high enantioselectivity with a general substrate tolerance. Similarly, a collaboration between the Miller, Sigman, and Lin groups afforded the optimization of the electrocatalytic synthesis of chiral lactones from meso‐diols using peptidyl aminoxyl radicals.^{[ 97 ]} Their optimization campaign explored seven different catalysts across 15 different diols that showcased substrate diversity with a compatible oxidant. These examples highlight how informed design strategies can lead to strategic reaction outcomes.

In the pursuit of reaction discovery in HTE, designing experiments is not as straightforward due to the difficulty of predicting novel reactivity; however, several reports have recently advanced this area (Figure 3c).^{[ 79} , ⁹⁸ , ^{99 ]} In a foundational study in 2011, the MacMillan group utilized HTE to evaluate reactions by pairing substrates bearing complementary functional groups in the same well. Through an “accelerated serendipity” approach, a new photoredox catalyzed α‐amino C–H arylation reaction was discovered.^{[ 41 ]} In a contemporary study, the Hartwig group disclosed a multidimensional screening strategy for the high‐throughput discovery of metal‐catalyzed coupling reactions.^{[ 42 ]} In this work, a diverse mixture of substrates (17 reactants per well) was subjected to different catalyst and ligand combinations. Through preliminary analysis using gas chromatography (GC)‐MS or electrospray ionization (ESI)‐MS, peaks that exceeded the mass of any single substrate were identified. This allowed them to narrow their search to the most promising metal and ligand combinations. This was followed by a deconvolution strategy that involved systematically testing individual reactants and monitoring product formation to identify the coupling partners. In this work, a copper‐catalyzed alkyne hydroamination and two nickel‐catalyzed hydroarylation reactions were disclosed. More recently, the same group presented a more advanced deconvolution strategy coined “snap deconvolution” that provides a platform to identify novel reactivity through automated interpretation of mass data.^{[ 100 ]} Using this method, a novel alkyne hydroallylation and a nickel‐catalyzed alkyne diarylation were reported. These pioneering studies prioritize broad reactivity coverage for unbiased exploration whereby the plates are constructed to maximize diversity while maintaining compatibility with miniaturization.

Another distinctive example of design‐driven discovery in HTE was reported by the Huang group.^{[ 101 ]} They developed an innovative strategy in which product isolation was a primary consideration. The reaction design was built around a quaternary ammonium tagging strategy that incorporates a cationic functional group on one of the reactants such that any product formed will also carry a charge. This modification allows for easier identification of product formation as these molecules can be selectively captured using ion‐exchange resins. Notably, this approach allowed for the discovery of an unreported C–N coupling transformation upon screening 1536 reactions. Overall, this strategy underscores how new design principles in HTE can facilitate the discovery of novel reactivity.

Although these strategies to accelerate reaction discovery are promising, they still depend on chance and assume that chemical reactions are discrete events unaffected by other ongoing processes. An early effort to provide an alternative method to reaction development was introduced by the Pfaltz group in 2004, which emphasizes understanding catalytic interactions with a focus on identifying chiral catalyst intermediates and their potential for enantioselectivity.^{[ 102 ]} Inspired by this work, the Glorius group in 2016 introduced a “mechanism‐based” approach for reaction discovery where a key step of a mechanism is used to guide the design of experiments for a general reaction class (Figure 3d).^{[ 103 ]} Rather than covering reactivity space broadly, as a proof of concept, the group devised a two‐stage screening strategy for the discovery of novel photocatalytic reactions. In their report, luminescence spectroscopy was used to uncover new classes of quenchers. In one example, 1H‐benzotriazole was identified as a new promising quencher for fac‐Ir(ppy)₃. Using HTE, this combination was employed in the de‐nitrogenation of benzotriazoles. Additionally, the Glorius group presented the first example combining both “reaction‐based” and “mechanism‐based” approaches and was able to develop two visible‐light driven reactions: an energy‐transfer dearomative [2 + 2]‐cycloaddition of heterocycles and a [2 + 2]‐cycloaddition to alkynes.^{[ 104 ]} With this two‐dimensional screening strategy, accelerated reaction discovery was possible and insightful mechanistic knowledge was acquired, which can be utilized for reaction and catalyst designs. This approach provides a complementary tool for reaction discovery which is simple, fast, and versatile. Overall, implementing these strategies to address limitations in HTE design have aided in the exploration of chemical space while accelerating the process of reaction optimization or discovery and reducing bias in selection of reagents and catalysts.

2.2. Experimental Execution

Unlike HTS, which has relatively uniform and predictable workflows, the execution of HTE introduces its own set of challenges due to the need for flexibility to accommodate different chemistries and conditions. Thus, to handle a wide variety of reagents on small scale, it is critical to ensure standardization while maintaining efficient throughput. Various simple yet effective strategies, including the use of large volumes of dilute solutions for dispensing or running multiple replicates and averaging reaction outcomes have been developed to mitigate error in HTE studies. However, further miniaturization of experiments necessitates the integration of automation into the workflow to ensure consistent and reliable results. Robotic handling can afford the execution of repetitive tasks with better accuracy and reproducibility than manual addition using the same stock solutions/reagents.^{[ 10 ]} Additionally, the innovative ways of executing HTE have become integral for the field to adopt different chemical methods including photo‐ and electrocatalytic strategies. Thus, to enhance experimental outcomes and ensure standardization, advances in plate design, flow and microfluidics, ultra‐HTE, and automation have been instrumental.

Generally, HTE reactions are performed in 24‐well, 96‐well, or 384‐well plates that are commercially available from suppliers such as Analytical Sales^{[ 62 ]} and Chemglass.^{[ 63 ]} For further miniaturization, ultra‐HTE using 1536‐well plates enables nanomole‐scale reactions.^{[ 16 ]} These MTPs were initially designed for thermal reaction screening, which can be augmented with equipment like V&P Scientific's^{[ 105 ]} or Singh Instrument's^{[ 106 ]} stirring bays. As photocatalysis and electrocatalysis gain momentum in synthetic reaction development, it has also become necessary to adopt HTE infrastructures that accommodate light and electrochemical variability. For photochemical transformations, Analytical Sales’ para‐dox aluminum blocks and Lumidox LED arrays^{[ 107 ]} technologies offer affordable platforms to conduct light‐driven high‐throughput reactions. Similarly, electrochemistry in HTE has become more common.^{[ 108 ]} An early report by the Yudin group developed a 16‐well spatially addressable electrolysis platform (SAEP) for the parallel electrosynthesis of small organic molecules.^{[ 109 ]} Inspired by this, ElectraSyn and IKA screening platforms, such as e‐hive,^{[ 110 ]} have simplified and popularized electrochemistry as a tool for organic synthesis by commercializing easy to use equipment to perform cyclic voltammetry (CV) and electrolysis of multiple reactions at the same time. However, these systems are not compatible with standard HTE automation, making it difficult to streamline current workflows. To address this challenge, the Lin group in collaboration with Merck developed a standardized microscale reactor, high‐throughput for electrochemistry (HTe‐Chem), that seamlessly integrates electrochemistry into HTE infrastructure.^{[ 111 ]} In this setup, each row in a 4  ×  6 format is connected in series and allows screening of four distinct current or potential values. All the relevant electrochemical parameters like electrode material, electrolyte, applied potential, and applied current can be screened along with the capability to screen traditional reaction conditions such as temperature, solvent, and catalyst. Recently, the Lin group also introduced a novel approach for wireless electrosynthesis using nanofabricated microelectronic devices, termed SPECS (small photoelectronics for electrochemical synthesis).^{[ 112 ]} This user‐friendly device integrates with existing infrastructure and facilitates microscale high‐throughput electrochemical synthesis. Furthermore, the Baker group reported an electrochemical cell array, Legion, whereby the potential of each well in a 96‐well plate can be controlled individually giving rise to a high‐throughput platform for electrochemical synthesis and analysis.^{[ 113 ]} Overall, these advances provide powerful tools with immense potential, owning to their flexibility and accessibility to accelerate the wider adoption of electrochemistry in organic synthesis.

In addition to performing reactions in MTPs, strategies that adopt flow chemistry and microfluidics have been repurposed to increase throughput and complement traditional HTE, performing reactions in a continuous, scalable, and cost‐efficient manner.^{[ 114} , ¹¹⁵ , ¹¹⁶ , ^{117 ]} Flow chemistry offers several advantages by providing better control over reaction conditions, diminishing solvent evaporation, enhancing safety, and providing access to otherwise inaccessible high pressure or temperature.^{[ 118 ]} Furthermore, this technology can be integrated with in‐line analytical tools that can facilitate automated execution and analysis of reactions in a closed‐loop approach. Building on these advances, lab‐on‐a‐chip platforms and total analysis systems (TAS) have emerged as powerful miniaturization tools that integrate reaction, separation, and analysis steps on a single device.^{[ 117 ]} The development of microfluidics, a branch of flow chemistry, has provided a sustainable approach to HTE by minimizing consumption of reagents from micromoles to nanomoles (Figure 4d). Early work in the application of both flow chemistry and microfluidics to an automated HTE workflow was pioneered by the Jensen and Jamison groups.^{[ 119} , ¹²⁰ , ^{121 ]} Their work involved developing microfluidic devices for the closed‐loop optimization of target molecule synthesis. In 2018, the Sach group reported the application of flow chemistry using an automated flow‐based approach for reaction optimization and synthesis.^{[ 122 ]} Their strategy allowed for the execution of over 1500 reactions at the nanomole scale for a Suzuki–Miyaura coupling reaction in flow within 24 h. This seminal work demonstrated that consistent stoichiometries of reaction components can be achieved by injecting a concentrated reactant and reagent stock solution prepared in different solvents and diluted with a suitable carrier solvent in a continuous manner. This overcame a major hurdle in flow chemistry that required preparation of different stock solutions of the same reagents whenever solvent screening is performed.

Figure 4.

High‐throughput experimentation (HTE) workflow and different platforms for execution of reactions. a) Liquid handlers. b) Solid dispensers. c) Automated workstations that centralize key operations for HTE. d) Flow/microfluidic platforms. Images of instruments in this figure have been used with permission from respective manufacturers. Image © Tecan Group Ltd. All rights reserved. Used with permission.

While it can be engineering intensive, flow chemistry has been adopted for executing photochemical reactions.^{[ 123} , ¹²⁴ , ^{125 ]} By continuously passing materials through irradiated tubing, photon flux is increased, which enhances reactivity and yields.^{[ 126 ]} Thus, the development of high‐throughput flow‐based methods for photocatalysis is particularly intriguing. With this aim, the Stephenson group disclosed a nanoliter size reaction droplet microfluidic platform that allows for continuous high‐throughput discovery at the picomole scale.^{[ 127 ]} The system was automated to dispense reagents and was coupled to high‐throughput ESI analysis, which also provided a 10‐fold increase in analysis throughput in comparison to other ESI‐MS‐based flow reaction analysis. This method was utilized to generate pharmaceutically relevant compound libraries with improved material and time efficiency. In a seminal report, the Noël group developed a flow‐based autonomous chemical synthesis robot, RoboChem.^{[ 128 ]} In this work, the self‐optimization of reactions is accelerated and streamlined by integrating several hardware and software. The effectiveness of this platform was highlighted through the optimization of versatile photocatalytic reactions such as C–H alkylation, oxytrifluoromethylation, and C–C coupling reactions. Another complementary approach to flow chemistry was communicated by the MacMillan group in 2021 to address the challenges of translating conditions optimized in microscale HTE platforms to flow systems.^{[ 129 ]} In this work, an HTE flow simulation (FLOSIM) device was designed to mimic the flow setup while using 96‐well plates. This was achieved by matching the solution height in the HTE vials to the internal diameter of the flow tubing and using concave lenses with Kessil lamps to reflect the light in all directions and increase the photon flux. The report validates the effectiveness of this method by demonstrating the efficient translation of the yield of four different photocatalytic reactions conducted using the FLOSIM and compared to a flow reactor. Overall, flow and microfluidic platforms have proven to be crucial in reaction optimization especially for industrial process development.

To improve efficiency and reproducibility of reaction setup and reagent delivery, synthetic chemists have adopted liquid and solid dispensing systems that are compatible with a range of organic solvents. Common and affordable liquid handling instrumentation include Opentrons’ OT‐2^{[ 64 ]} and Flex systems,^{[ 130 ]} Waters’ Andrew⁺ robot,^{[ 131 ]} and Agilent's Bravo platform.^{[ 132 ]} These systems provide user‐friendly interfaces that can handle microliter to milliliter volumes and are particularly useful for reaction workup and sample preparation for analysis (Figure 4a). Additionally, more advanced systems such as the ones offered by Unchained Labs^{[ 133 ]} and Beckman Coulter^{[ 134 ]} provide more flexibility and precise handling of liquids using needle‐based dispensing (Figure 4c). With the advent of ultra‐HTE, the execution of reactions at the nanomole‐scale necessitates the integration of specialized automated liquid handling systems. This miniaturization introduces additional challenges such as restricted solvent choices due to volatility, limited stirring capabilities, and increased likelihood of errors. The use of robotics, such as the SPT Labtech Mosquito,^{[ 135 ]} which precisely dispenses nanoliter volumes, mitigates some of these concerns by combining multicomponent mixtures into homogeneous drops without the need for stirring. However, oftentimes DMSO is used as the solvent to minimize evaporation, which restricts the variables that can be altered. Additional platforms such as Tecan's Fluent dispensing system^{[ 136 ]} or Beckman Coulter's Echo Acoustic liquid handler^{[ 137 ]} are utilized for ultra‐HTE sample preparation.

Beyond liquid handling, automation of solid dispensing comparatively poses more challenges due to the variable physical properties of solids. Solids can be light, fluffy, heavy, electrostatically charged, hygroscopic, or granular which makes them difficult to handle and more prone to error in measurements. However, to accelerate the reaction setup, integrating a solid dispensing platform that can accurately and precisely dispense solid components is essential. Thus, several automated powder dispensing tools have been designed and are commercially available with a wide range of dispensing capacities. Systems such as Chemspeed SWING^{[ 138 ]} and Mettler Toledo XPR Automatic Balance^{[ 139 ]} prove to be more accurate at dispensing large masses and are not ideal for small quantities of target molecules (Figure 4b,c).^{[ 140 ]} Advances have led to systems (Chemspeed FLEX,^{[ 141 ]} Chemspeed SWILE,^{[ 142 ]} Mettler Toledo CHRONECT,^{[ 143 ]} and the Unchained Labs^{[ 133 ]} platform) that can effectively measure low to sub‐milligram quantities (Figure 4b,c). However, all the systems mentioned cannot dispense nanomole amounts of solids with acceptable error.^{[ 144 ]} To overcome this challenge, Chembead technology has been introduced by AbbVie as an innovative solution that addresses the need to dispense sub‐milligram and microgram quantities of solids with different properties by coating small glass beads with chemicals (Figure 4b).^{[ 145} , ^{146 ]} This technology provides a versatile alternative to dispensing solids for HTE that has been proven effective for diverse reaction setups for both academia and industry.^{[ 147} , ^{148 ]}

While these advances have significantly enhanced and accelerated the workflow of HTE, the execution of reactions still remains distributed among different systems making it challenging to track the different steps (dispensing, weighing, heating/cooling, and stirring) in a seamless manner. Thus, more advanced systems such as the ones offered by Unchained Labs,^{[ 133 ]} Beckman Coulter,^{[ 134 ]} and Chemspeed^{[ 141 ]} provide more flexibility (Figure 4c). These platforms allow for the coordination of multiple steps including dispensing, stirring, and temperature control within a unified setup. Adopting such integrated systems helps centralize key operations, reducing manual intervention and enhancing reproducibility. Together, these conceptual and technological innovations have significantly facilitated the execution of HTE and lay a strong foundation for more versatile, scalable, and automated platforms for reaction optimization and discovery.

2.3. High‐Throughput Analysis

As execution of HTE has significantly advanced, there is an emerging need for data analysis to be accelerated such that it does not become the bottleneck in the HTE workflow.^{[ 149 ]} In contrast to HTS, HTE demands a broad and diverse range of analytical techniques that vary with reaction types and their products. Additionally, as the scale of the reaction decreases, the quantity of material becomes limited, requiring techniques that are very sensitive and suitable for low‐volume analysis. As a result, effective high‐throughput analysis (HTA) requires not only rapid data generation but also the ability to analyze diverse reaction outputs with high accuracy and reliability. This includes detecting subtle variations in outcome and integrating multiple analytical tools. By balancing speed with comprehensive analysis, HTA enables efficient decision‐making and maximizes insights gathered per reaction. Thus, advancements in instrumentation have been essential to propel high‐throughput workflows.

MS has been a privileged technique widely adopted in industry and academia due to its sensitivity and versatility. Moreover, MS can provide robust characterization for simple or complex molecules and can be readily integrated to other analytical instruments. Traditionally, chromatography coupled with MS such as liquid chromatography (LC)‐MS, gas chromatography (GC)‐MS, and chiral supercritical fluid chromatography (SFC)‐MS has been employed for the analysis of organic reactions. However, typically run times are in the range of 5–15 min/sample due to a separation component, which is not suitable for HTA.^{[ 29 ]} Thus, significant efforts have been dedicated to improving the analysis time of these techniques to meet the needs of HTE.

One of the early advances in HTA was the introduction of ultra‐high‐performance liquid chromatography (UHPLC), which allowed for faster analysis of samples, with some methods achieving run times of less than a minute. This technique continues to be used in many academic and industrial labs as the standard HTE form of analysis (Figure 5a).^{[ 150 ]} With the introduction of ultra‐HTE or automated platforms with multiple 96‐well plates, there is a challenge to further reduce the analysis time. Ultra‐HTA would necessitate analysis to reach timeframes closer to 1 s/sample. In 2010, researchers at Merck reported an innovative technique for chromatography, multiple injections in a single run (MISER), that improves the throughput of analysis of HPLC‐MS methods.^{[ 53 ]} Through flow injection analysis (FIA), multiple samples are injected in a single run without the need to stop analysis for every new sample. The system continuously collects data and compiles all information into one single uninterrupted chromatogram (misergram) that serves as direct visual representation of the data. Selected ion monitoring (SIM) makes this technique particularly useful for the analysis and comparison of samples with identical ions of interest. However, due to the lack of a full‐scan mode, analysis of unknown side products can be very challenging. MISER‐based techniques have found wide application in synthetic methodology for analysis of various reactions in parallel and have been applied to LC, LC‐MS, GC‐MS, and chiral SFC‐MS analysis.^{[ 30 ]}

Figure 5.

High‐throughput analysis (HTA). a) Comparison of analysis time per sample for various analytical mass‐based techniques. b) Application of Desorption Electrospray Ionization‐Mass Spectrometry (DESI‐MS) for HTA in organic methodology. Gas Chromatography (GC), Liquid Chromatography (LC), Supercritical Fluid Chromatography‐Mass Spectrometry (SFC‐MS), Ultra‐High‐Performance Liquid Chromatography (UHPLC), Multiple Injections in a Single Experimental Run (MISER), Acoustic Droplet Ejection‐Open Port Interface‐Mass Spectrometry (ADE‐OPI‐MS), Nanoelectrospray Ionization (nESI), Mass Spectrometry (MS).

Another strategy to increase throughput of analysis involves a MultiplexMS approach whereby pooling multiple reaction samples for simultaneous analysis is conducted in a single MS run.^{[ 151} , ^{152 ]} With the use of a deconvoluting software, qualitative information on promising results is obtained, allowing for follow‐up analysis to focus on the successful wells. In cases where analytes possess distinct masses with little interference, pooling can be a powerful technique to greatly accelerate analysis. For example, the Jacobsen group applied this approach in asymmetric catalysis to identify enantioselective catalysts. Multiple chiral products (8 distinct compounds) from an HTE experiment were pooled together based on unique masses and analyzed using SFC‐MS.^{[ 96 ]} Rather than individually analyzing each well, this technique allowed for accelerated selection of catalysts that were promising. In 2015, seminal work from Merck demonstrated the power of integrating MISER LC‐MS and pooling strategy into their workflow for rapid analysis of 1536 Pd‐catalyzed C–N coupling reactions in just 2.5 h.^{[ 17 ]}

Hyphenated MS technologies that involve chromatography for separation have improved substantially, allowing sample analysis to take between 30 and 60 s/sample. However, to achieve analysis as fast as sub‐second/sample, stand‐alone MS analysis with its speed, sensitivity, and accuracy emerges as a promising strategy. In that direction, ambient ionization mass spectrometry (AIMS), whereby samples deposited onto a matrix or plate are analyzed under ambient atmosphere, has revolutionized analytical throughput.^{[ 29} , ^{30 ]} Matrix‐assisted laser desorption ionization (MALDI)‐MS is a widely used technique for high‐throughput analysis of biomolecules and large organic molecules that tend to fragment upon ionization with conventional methods. One notable example from the Dreher group demonstrated the use of MALDI‐MS to analyze 1536 nanomole scale C–N coupling reactions in less than 10 min to investigate a broad chemical space.^{[ 99 ]} This approach underscores the potential of integrating ultrafast MS analysis with HTE to rapidly evaluate reaction outcomes. However, the analysis of small organic molecules with MALDI is challenging due to the interference of the matrix molecules.^{[ 153 ]} This can be overcome by AIMS methods that circumvent the need for a matrix with little to no sample preparation.

One notable advance in HTA, desorption electrospray ionization (DESI)‐MS, was pioneered by the Cooks group in 2004.^{[ 154} , ^{155 ]} A key feature of this technique is the ability to ionize chemical species on solid surfaces under ambient atmosphere without the need for prior sample preparation, which contrasts with traditional solution phase analysis (Figure 5b). Through a collaboration with the Thompson group, DESI‐MS was translated for initial applications in high‐throughput analysis of organic reactions based on its imaging capabilities.^{[ 156 ]} In that system, samples were deposited onto an inert surface and individually analyzed to generate an ion intensity map with sample analysis time of around 3 s/sample. This has been successfully applied for accelerated screening of N‐alkylation and Suzuki‐coupling reactions.^{[ 156 ]} Moreover, significant efforts have been dedicated to integrating automation and analysis software. Specifically, the chemical reaction integrated system (CHRIS) software provides automatic control over the DESI system and streamlines the search for m/z values of molecules of interest.^{[ 157 ]} Ultimately, DESI‐MS has been applied to a wide range of reaction classes and has been a main contributor to HTA (Figure 5b).^{[ 158} , ^{159 ]}

In efforts to fully avoid any sample preparation including spotting on a surface, acoustic droplet ejection (ADE) technology coupled with MS has been implemented for HTA.^{[ 160 ]} The ADE platform allows for the ejection of charged nanodroplets directly from the reaction mixture in a plate through an open‐port interface (OPI) using a transfer capillary into an MS system for ionization and analysis. One thorough study by the Zhang group at Pfizer takes advantage of this technology for ultra‐HTE reaction screening for drug discovery applications.^{[ 161 ]} They first tested it for a Pd‐catalyzed C–N coupling reaction and then for nanoscale parallel medicinal chemistry reactions ranging from small to complex molecules. Utilizing ADE‐OPI‐MS allowed for the analysis of 384 samples in less than 15 min with minimum sample consumption and a broad range of detection (Figure 5a). These properties when coupled with high‐resolution MS (HRMS) provide a powerful platform that has potential to generate high quality data at reasonable speed for HTA and ultra‐HTA.

To further increase the throughput of analysis, the integration of a microfluidic droplet generator with nano‐ESI (nESI) has been developed.^{[ 162 ]} With the capability to generate thousands of drops per second, droplet‐based microfluidic when integrated with nano‐ESI can yield a throughput of up to 30 droplets/s for MS detection of samples as small as 65 pL. One notable work was reported by the Kennedy group where they showcased the high‐throughput analysis of transamination reactions by interfacing microfluidic chips to nESI emitters.^{[ 162 ]} This technique was then applied by the same group in collaboration with the Stephenson group for the rapid analysis of photoredox reactions for drug discovery application and accessing late‐stage functionalization libraries.^{[ 163 ]} Using this protocol, they were able to achieve a throughput as high as 2.9 samples/s.^{[ 163 ]} This seminal work demonstrates the promising technologies that can be integrated into HTE workflow to further extend the limits of MS analysis. Furthermore, flow chemistry has been successfully integrated with a range of analytical tools, expanding the scope and flexibility of monitoring reactions within HTE. For example, to address the limiting throughput of NMR for HTA, the Hankemeier group reported utilizing flow‐NMR, which circumvents the need for laborious sample preparation.^{[ 67 ]} In their work, samples were automatically transferred from 96‐well plates to a 500 or 600 MHz NMR spectrometer. This platform provides a route for fast and robust NMR screening studies.

HTA has been primarily utilized to assess product yield and selectivity; however, gathering a plethora of information from cyclic voltammetry (CV), Stern‐Volmer studies, IR analysis, and in situ reaction monitoring is important. This shift toward “data‐rich” analysis enables a more comprehensive understanding of reactions and increases the probability of successful outcomes. As a result, several efforts have been directed toward the development of methods and tools to attain more diverse data from HTE samples. Legion, mentioned in section 2.2, allows for collecting CV data for each individual experiment in a 96 well‐plate.^{[ 113 ]} The design and dimension of this instrument matches with a standard 96‐well plate, which facilitates its integration into a typical HTE framework. A significant and common experiment to probe photochemical reactions is fluorescence quenching studies through Stern–Volmer analysis. Though a valuable experiment to conduct, Stern–Volmer analysis is time consuming, and these experiments are sensitive to air due to the interference of O₂ as a quencher. In 2018, the Noël group introduced a fully automated and flexible continuous‐flow‐based platform to monitor the absorbance through in‐line UV–Vis to study the effect of a quencher on a photocatalyst.^{[ 164 ]} This flexible platform facilitates data collection and storage with easy access to a quencher library of Stern–Volmer constants of known photocatalysts. However, this custom‐made flow setup lacks the throughput required to keep up with the exponentially increasing chemical space. To overcome this, Ruccolo's group recently provided a complimentary batch‐based approach for high‐throughput analysis of Stern–Volmer constants by using a commercially available 96‐well plate and fluorescence plate reader that can be easily fit inside a glovebox to provide inert atmosphere.^{[ 165 ]} To validate their method, Stern–Volmer constants for 20 common photocatalysts with 11 common quenchers (a total of 220 quenching constants) were generated by analyzing 1920 samples in approximately 2 h. Commercially, available high‐throughput spectrophotometers such as the BMG LABTECH plate reader^{[ 166 ]} can be utilized to accelerate data collection.

The translation of platforms and strategies to conduct kinetic studies that are compatible with HTE setups can be foundational for “data‐rich” experimentation. These studies improve the understanding of reaction mechanisms, uncover reaction rates, assess activity of catalysts, and provide a metric to predict reaction performance. In fact, several groups have started incorporating high‐throughput kinetic analysis into the HTE workflow. In 2018 researchers at GSK conducted kinetic experiments for the development of an API using an Unchained Labs platform equipped with the optimization sampling reactor (OSR) module, which can monitor reaction temperatures, stirring rates, and gas pressures.^{[ 87 ]} In another instance, GSK reported a high‐throughput kinetics protocol that provided a platform for gathering time course data for individual reactions in a 48‐well plate to generate a kinetic model in less than a week.^{[ 167 ]} This work investigated an aza‐Michael reaction based on reaction kinetic analysis approaches effectively applied by the Blackmond group.^{[ 71} , ¹⁶⁸ , ^{169 ]} In another report by the Chirik group, an HTE‐driven method of continuous variation (Job plot) was applied to determine the composition of catalytically relevant complexes.^{[ 170 ]} Their strategy provided mechanistic insights without the need for isolating an unstable intermediate. Through this approach, they uncovered an unusual trimetallic Ni complex as the active species for stereoselective alkene hydrogenation, demonstrating the utility of HTE not only to discover novel reactions but also to rapidly reveal unique mechanisms. Overall, these advancements and ongoing efforts in HTA are integral to ensure that data processing keeps pace with the growing speed and complexity of HTE. By integrating fast, automated, and diverse analytical techniques with ML, complex data sets can be routinely handled, providing critical reaction insights in addition to accelerated outcomes.

2.4. Data Management and Application

When multiple reactions are run in parallel as in the case of HTE, large and diverse datasets including rich metadata, spectra, and graphics are generated. Depending on the nature of the experiments, these procedures, experimental parameters, and reaction outcomes can contribute to several gigabytes (GB) or up to terabytes (TB) worth of data within a short span of time.^{[ 171 ]} To manage this scale of data, it is crucial to not only store information systematically but also to develop methods to process, structure, and retrieve it efficiently. In traditional OVAT screening, data handling can be addressed manually, especially when only a few reactions are examined at a time. Although manual storage through electronic lab notebooks (eLN) remains an option, it can be inefficient, unsystematic, and limited in terms of the metadata collected. This becomes more significant as HTE becomes increasingly complex with “data‐rich” setups.

To systematically track experimental procedures and their details, automated instruments include software that enable users to download and save comprehensive information on the experiment such as plate layout, reagent information, and detailed procedures (Figure 6a). This information is crucial for enabling adjustments to the protocols in the future, ensuring reproducibility, and linking procedure details to reaction outcomes. To facilitate this, several software associated with different platforms have been developed. For instance, Unchained Labs offers Laboratory Execution and Automation (LEA) software^{[ 10} , ^{172 ]} that centralizes all experimental protocols and reaction details to ensure traceability and data retrieval. Similarly, Chemspeed has developed ARKSUITE^{[ 173 ]} and Beckman Coulter employs CytExpert^{[ 174 ]} software to control instrument operations and data collection.

Figure 6.

Data storage, management, and application. a) Examples of software that provides storage of experimental metadata. b) List of common software that is utilized for data analysis and/or visualization. c) Application of high‐throughput experimentation (HTE) data toward predictive chemistry utilizing machine learning (ML).

Alternatively, whether working with an automated workstation or integrating separate instruments, the Cernak group developed a user‐friendly web‐based software, phactor.^{[ 43 ]} This platform allows for rapid planning of HTE reactions and stores all chemical and metadata in machine‐readable formats that can be readily integrated with other software. In another report, researchers at Roche shared an open‐access HTE Operating Software (HTE OS) that integrates all data and instruments into one single workflow from experiment design to data storage.^{[ 175 ]} A common solution in industry is the use of Katalyst D2D^{[ 92 ]} and ChemAnalytical Workbook^{[ 176 ]} by ACD/Labs that can be customized to support entire workflows. Additionally, Spectrus Platform^{[ 177 ]} provides a collection of software to process, interpret, and report data obtained from diverse sources. In addition, analytical integration is essential to gather meaningful insights from experiments. HTE can be integrated with a wide range of analytical instruments such as MS, IR, and UV–Vis. This data can be collected and imported into the same unified data management systems discussed earlier or stored separately based on user preference. By centralizing the data management, platforms can reduce the need for manual data entry, minimize error, and enable efficient comparison between different reaction parameters and outcomes.

While handling large and complex datasets, another important consideration is the integration of software that can automate data analysis and processing at a reasonable timeframe to maintain high throughput. One comprehensive software that is commonly used for automated analysis of high‐throughput chromatography‐MS data is Virscidian.^{[ 178} , ¹⁷⁹ , ^{180 ]} This software provides the opportunity to analyze, visualize, manage, and share data from different instruments in a seamless manner. Other analytical software includes Dotmatics Luma^{[ 181 ]} which has been used to efficiently process raw data from HTE reactions. In addition to these options, open access platforms can expedite automated data analysis. One such example was presented by the Jensen group in collaboration with Bayer AG for an open‐source platform (MOCCA) to automate HPLC chromatogram analysis.^{[ 182 ]} They demonstrate the ability of MOCCA to be incorporated into a workflow for closed‐loop optimization and reaction discovery, whereby experimental data can be iteratively used to adjust and enhance outcomes. These advances hold great promise for the analysis of HTE data in a more robust and standardized manner.

With a large amount of data collected, it is important to be able to efficiently visualize this data for ease of interpretation.^{[ 183 ]} Statistical tools that can also help to identify patterns and correlations in an interactive fashion would be beneficial to gather better insights from each set of experiments. For this purpose, software such as TIBCO Spotfire,^{[ 184 ]} JMP (SAS),^{[ 89 ]} CROW,^{[ 185 ]} Tableau,^{[ 186 ]} HTSvis,^{[ 187 ]} and Microsoft Power BI^{[ 188 ]} can be utilized (Figure 6b). These tools allow users to visualize data beyond simple correlations through manipulation to generate scatter plots, cluster‐based groupings, and interactive 3D visualizations. These can reveal multidimensional relationships between reaction conditions and reaction outcomes. Importantly, these plots can be stored alongside raw data as representations of those reaction features and yields.

Once data has been collected and structured, the ability to share this data seamlessly between select users and institutions has become increasingly important for scientific progress. Classical data sharing through the cloud such as Amazon Web Services (AWS) or Oracle can be generally used for the management and storage of HTE data. However, this can be limiting due to concerns regarding data security, accessibility, and the high cost associated with cloud storage.^{[ 189} , ^{190 ]} It is worth noting that sharing data across multiple groups and industries can be challenging due to the different preferences in data storage and sharing platforms along with privacy concerns when exchanging sensitive information. In an effort to provide a common platform to store and share data in a structured and standardized manner, analogous to CCDC for X‐ray structures or PDB for protein structures, the “Open Reaction Database” (ORD) initiative was conceptualized in 2021.^{[ 191} , ¹⁹² , ^{193 ]} This database aims to encourage a culture of data sharing among synthetic chemists while providing users with open access to validated data which curates not only batch chemistry experiments but HTE experiments as well. Notably, initiatives like this one can only succeed via enthusiastic and ethical participation of the scientific community. A key aspect of such efforts is the need for comprehensive libraries and structured approaches, similar to HTS, particularly for synthesis and catalysis where large‐scale data management is crucial for driving meaningful insights.^{[ 194 ]} Although ORD is very promising, future advances in filtering and validating the data and gaining public trust and publication support will be necessary to improve its utility. Additionally, such a platform that includes both positive and negative data is crucial for developing a better understanding of systems being studied and for preventing redundant experimental efforts.

HTE generates masses of both raw and processed data that can become time and resource intensive to store and share. Thus, extremely powerful central processing units (CPUs) and graphics processing units (GPUs) are required. To overcome this challenge, platforms like Apache Spark^{[ 195 ]} and TensorFlow that operate on the Hadoop distributed file system (HDFS) have been utilized.^{[ 26 ]} With these approaches, data can be distributed into fragments (nodes), which are trained parallelly on different cloud‐based services to generate local parameters. These local fragments combined can then generate global parameters that can be interfaced between nodes. This method provides an efficient strategy to accommodate massive databases, which are required for the effective training of ML models. Moreover, this distributed nature of HDFS allows several research groups to accommodate and access large datasets and process distinct parts of the data simultaneously, which contributes to collaborative research. As HTE continues to evolve, leveraging computing and cloud‐based resources will be essential to efficiently manage large and complex datasets.

With this data infrastructure, one emerging role of HTE is to provide access to large datasets that can effectively train ML models (Figure 6c). These models can be trained to identify patterns and recommend conditions to optimize reactions and discover novel transformations. Pioneering work by several groups such as the Doyle, Denmark, and Cernak groups have highlighted how combining HTE with data‐driven methods can lead to accelerated and more reliable predictions and development.^{[ 16} , ⁴³ , ⁹⁵ , ¹⁹⁶ , ¹⁹⁷ , ¹⁹⁸ , ^{199 ]} Moreover, several software are available, such as Sunthetics^{[ 200} , ^{201 ]} and Quantum Boost,^{[ 202 ]} that employ ML strategies to rapidly optimize reaction parameters. These efforts reflect a growing interest in data centric and predictive chemistry in synergy with HTE.^{[ 9} , ^{203 ]} However, continued improvement in data quality and accessibility is a pre‐requisite for achieving accurate ML models. Therefore, efforts to standardize HTE data storage for easy access, reproducibility, and shareability are essential to realize data‐driven chemistry.

3. Future Outlook

Over the past several decades, HTE has become a transformative approach not only for compound library synthesis but for accelerating optimization and discovery of reactions in academia and industry. This advancement has been driven by significant collaborative efforts to innovate reaction design, experimental execution, reaction analysis, and data management. Although the HTE workflow has become significantly more accessible to general scientific practitioners, there is still room for improvement to achieve the full potential of HTE in organic chemistry.

For many laboratories outside of large established companies, the required finances to establish and maintain an HTE workflow or facility presents a high barrier to adoption. To enable non‐automated HTE, MTPs, stirrers, dispensers, and analytical instruments are needed, and, to facilitate a partial or fully automated HTE system requires a several hundred‐thousand‐dollar workstation, integration of analysis equipment, and software capabilities, which go far beyond the cost of start‐up funding or small companies’ budgets. Internationally, there has been a significant interest and investment in HTE infrastructure.^{[ 37 ]} There have also been examples of institutions investing in HTE as a core facility.^{[ 204} , ²⁰⁵ , ^{206 ]} The United States often funds basic or applied research and occasionally technology; however, few grants or universities would be able to maintain an established HTE core facility.^{[ 207 ]} Long‐term maintenance, replacement of consumables, and training can incur large costs for HTE that might not be covered even with outsourcing the use of these facilities. To maximize the use of HTE, funding is needed for shared equipment and instrumentation. Moreover, it would be beneficial for governments, private foundations, and universities to work together to support technological upgrades, data management solutions, and long‐term education of students and researchers.

In parallel to improving accessibility to advanced HTE facilities, a significant shift in modern HTE to conduct “data‐rich” experimentation that can be linked to reaction understanding and reaction prediction is crucial. Diverse analytical tools for HTA (NMR, UV–Vis, fluorescence, IR, and Raman spectroscopy) should be used more often for reaction monitoring, kinetics, and evaluation of reaction intermediates. Catalyst and reagent libraries that probe reactivity and stability are important to acquire to inform selection of appropriate conditions for reactions, and given the competitive nature of the field, encouragement of data sharing, both positive and negative results, consistent with FAIR principles is necessary.^{[ 18} , ^{19 ]} We envision that experimental information can be shared in the form of usable databases that can facilitate intra‐ and interinstitutional collaboration. To manage the search, the inclusion of various flexible filtering modes will be required to control the quantity and substance of the data shared. Additionally, a user‐friendly platform is needed that allows multiple labs to reproduce and validate data such that a robustness metric can be reported for each set of experiments. This metric will reflect how consistently an experimental procedure can be reproduced across different settings and users. Knowing the reproducibility of a data set with some certainty will ensure high quality training of ML algorithms. Moreover, partnerships between leading data industries such as Google and computational hubs can play a prominent role in curating data sets for HTE and fostering collaborations between academia and industry. Together, the aforementioned groups can establish a set of norms and ethical sharing practices in a responsible and inclusive manner.

To fully realize the potential application of HTE data, advances will be needed in ML. Currently, ML models commonly used in HTE workflows have limited interpretability. This hinders their utility for generating hypotheses and gaining mechanistic insights.^{[ 208 ]} Therefore, development of interpretable AI models will be crucial such that chemists can extract chemically meaningful information from them. Additionally, efforts to ensure those using HTE are aware of what kinds of data would be useful for ML algorithms will ensure broad and standardized data sets are collected for ML. With a higher level of clarity, transparency, and understanding on how HTE is being used for ML applications there can be an emphasis on bias reduction in data collection, and a greater likelihood that models will be successful.^{[ 209 ]}

An overarching vision for HTE is to use the advances in equipment, analysis, data, and ML to develop “self‐driving labs” (SDLs), such that the entire workflow of HTE can be conducted with minimal human intervention.^{[ 210} , ²¹¹ , ²¹² , ²¹³ , ²¹⁴ , ^{215 ]} Within a set workflow a computer would be capable of running reactions, analyzing them, and then selecting which reactions to conduct next based on prior data.^{[ 128} , ²¹⁶ , ^{217 ]} The invention of multipurpose robots has also served as effective transfer agents, to connect separate experimental steps and shuttle reactions from a synthetic workstation to a purification or analysis workstation.^{[ 218 ]} Recent advances in the field such as “Coscientist” from the Gomes group, introduce AI‐driven platforms that can semi‐autonomously design, execute, and optimize reactions.^{[ 219 ]} Additionally, pioneering work by the Kappe group has combined flow reactors with automation, in‐line analytics, and iterative protocols to enable closed‐loop optimization.^{[ 214} , ^{215 ]} This vision continues to evolve and has the potential to reform the way we approach chemical synthesis and reaction optimization. Currently, ideation is still required by a human being to initiate the workflow; however, the SDL concept is considered advantageous for redundant or routine tasks that help save researchers’ time. Currently, institutional hubs such as the Carnegie Mellon University (CMU) Cloud Lab^{[ 220} , ^{221 ]} and SwissCAT+^{[ 205} , ^{206 ]} are leading examples for making these platforms accessible to a community of scientists offering both training and research opportunities. We envision that establishing shared and standardized protocols, modular automation platforms, and collaborative efforts will be essential to ensure reproducibility and a broader adoption of such prototypes.

To ensure all organic chemists can take advantage of the benefits of HTE, it is imperative to establish local geographical hubs to provide community access to automated HTE platforms and databases. Several academic and international HTE centers have already demonstrated the value of shared HTE infrastructure with great promise.^{[ 204} , ²⁰⁵ , ²⁰⁶ , ²²² , ²²³ , ²²⁴ , ²²⁵ , ²²⁶ , ²²⁷ , ²²⁸ , ²²⁹ , ^{230 ]} However, an additional level of collaboration and modularity is necessary for expanded use of HTE. All HTE hubs should be equipped with flexible experimental and analytical capabilities for diverse research needs. Additionally, hubs should be connected and communicative in establishing best practices and norms for the field. Notably, some institutions have started integrating HTE into undergraduate curricula, although such efforts need to become more widespread and routine.^{[ 231 ]} Organic chemists should be exposed through hub‐sponsored workshops to state‐of‐the‐art technology. These workshops can be held in‐person or virtually to discuss basic principles of plate design, instrument integration, and use of data visualization and ML software. Moreover, there is common knowledge within specific labs, companies, and partner institutions that can be shared about which equipment and protocols work best for different chemistries, including photochemistry and electrochemistry. It is important to share knowledge of the use of certain reagents that may be challenging to weigh, dispense, or handle in liquid or solid dispensing systems, and, common sources of irreproducibility such as order of addition, concentration of reagents, or spatial effects in MTPs. Knowledge of workflows and software specifics for ML can also be shared. As HTE strategies evolve, emerging tools can be adopted broadly, helping identify solutions to diverse synthetic challenges. If these possibilities are realized, HTE can generate the data necessary to accelerate discovery in organic chemistry.

Conflict of Interests

The authors declare no conflict of interest.

Biographies

Reem Nsouli earned her B.Sc. in Chemistry from the Lebanese American University (LAU). She is a Ph.D. Candidate in Chemistry at Emory University in Professor Laura K.G. Ackerman‐Biegasiewicz's group. Her research focuses on the design of high‐throughput experimentation strategies for photocatalytic reaction discovery using Earth‐abundant first‐row metals. Additionally, she is also involved in the development of Fe and Ni metallaphotoredox catalysis.

Gaurav Galiyan earned his B.Sc. in Chemistry from Hindu College, University of Delhi, and his M.Sc. in Chemistry from the Indian Institute of Technology Kanpur. He is a Ph.D. candidate in Chemistry at Arizona State University under the supervision of Professor Anne K. Jones. His research focuses on the design and synthesis of inorganic complexes based on Earth‐abundant metals for electrocatalytic hydrogen production, with a keen interest in integrating high‐throughput experimentation to accelerate the discovery and optimization of catalytic methods.

Laura K. G. Ackerman–Biegasiewicz received her B.A. from Claremont McKenna College in 2009, working with Professor Anna G. Wenzel and Professor Andrew W. Zanella. She conducted research with Professor David A. Vicic after which she pursued her Ph.D. as an NSF graduate research fellow with Professor Daniel J. Weix at the University of Rochester. Laura was supported by an NIH postdoctoral fellowship at Princeton University advised by Professor Abigail G. Doyle. She is currently an Assistant Professor of Chemistry at Emory University where her lab focuses on informed high‐throughput experimentation strategies for first‐row metal catalysis.

Nsouli R., Galiyan G., Ackerman‐Biegasiewicz L. K. G., Angew. Chem. Int. Ed. 2025, 64, e202506588. 10.1002/anie.202506588

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.