Towards dial-a-molecule by integrating continuous flow, analytics and self-optimisation

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.

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.

Quid Pro Flow

How do you get into flow? We trained in flow chemistry during postdoctoral research and are now applying it in new areas: materials chemistry, crystallization, and supramolecular synthesis. Typically, when researchers think of "flow", they are considering predominantly liquid-based organic synthesis; application to other disciplines comes with its own challenges. In this Perspective, we highlight why we use and champion flow technologies in our fields, summarize some of the questions we encounter when discussing entry into flow research, and suggest steps to make the transition into the field, emphasizing that communication and collaboration between disciplines is key.

Inline purification in continuous flow synthesis – opportunities and challenges

Continuous flow technology has become the method of choice for many academic and industrial researchers when developing new routes to chemical compounds of interest. With this technology maturing over the last decades, robust and oftentimes automated processes are now commonly exploited to generate fine chemical building blocks. The integration of effective inline analysis and purification tools is thereby frequently exploited to achieve effective and reliable flow processes. This perspective article summarizes recent applications of different inline purification techniques such as chromatography, extractions, and crystallization from academic and industrial laboratories. A discussion of the advantages and drawbacks of these tools is provided as a guide to aid researchers in selecting the most appropriate approach for future applications. It is hoped that this perspective contributes to new developments in this field in the context of process and cost efficiency, sustainability and industrial uptake of new flow chemistry tools developed in academia.

A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives

Acceleration of material discovery has been tackled by informatics and laboratory automation. Here we show a semi-automated material exploration scheme to modelize the solubility of tetraphenylporphyrin derivatives. The scheme involved the following steps: definition of a practical chemical search space, prioritization of molecules in the space using an extended algorithm for submodular function maximization without requiring biased variable selection or pre-existing data, synthesis & automated measurement, and machine-learning model estimation. The optimal evaluation order selected using the algorithm covered several similar molecules (32% of all targeted molecules, whereas that obtained by random sampling and uncertainty sampling was ~7% and ~4%, respectively) with a small number of evaluations (10 molecules: 0.13% of all targeted molecules). The derived binary classification models predicted 'good solvents' with an accuracy >0.8. Overall, we confirmed the effectivity of the proposed semi-automated scheme in early-stage material search projects for accelerating a wider range of material research.

Bayesian optimization-driven parallel-screening of multiple parameters for the flow synthesis of biaryl compounds

Traditional optimization methods using one variable at a time approach waste time and chemicals and assume that different parameters are independent from one another. Hence, a simpler, more practical, and rapid process for predicting reaction conditions that can be applied to several manufacturing environmentally sustainable processes is highly desirable. In this study, biaryl compounds were synthesized efficiently using an organic Brønsted acid catalyst in a flow system. Bayesian optimization-assisted multi-parameter screening, which employs one-hot encoding and appropriate acquisition function, rapidly predicted the suitable conditions for the synthesis of 2-amino-2'-hydroxy-biaryls (maximum yield of 96%). The established protocol was also applied in an optimization process for the efficient synthesis of 2,2'-dihydroxy biaryls (up to 97% yield). The optimized reaction conditions were successfully applied to gram-scale synthesis. We believe our algorithm can be beneficial as it can screen a reactor design without complicated quantification and descriptors.

Simultaneous determination of enthalpy of mixing and reaction using milli-scale continuous flow calorimetry

A simultaneous determination of the enthalpy of mixing and reaction in a scalable continuous milli-scale flow calorimeter is investigated. As obtained calorimetric data is pivotal for the safety assessment of chemical reactions and processes. The acid-catalysed selective, homogeneous hydrolysis of acetic anhydride with half-lives from a few seconds to a few minutes is investigated as a model reaction. For the enthalpy of mixing 7.2 ± 2.8 kJ/mol and for the enthalpy of reaction −60.8 ± 2.5 kJ/mol were determined. For reactions that show complete conversion in the continuous reactor, a technique is introduced to further improve the accuracy of the reaction enthalpy determination. Thereby, the resolution of the observed temperature profile is increased by measuring the profile at different flow rates. Applying this procedure, the reaction enthalpy of −62.5 kJ/mol was determined which is in good agreement with literature values for this model reaction.

Graphical abstract

On-the-Fly Mass Spectrometry in Digital Microfluidics Enabled by a Microspray Hole: Toward Multidimensional Reaction Monitoring in Automated Synthesis Platforms

We report an approach for the online coupling of digital microfluidics (DMF) with mass spectrometry (MS) using a chip-integrated microspray hole (μSH). The technique uses an adapted electrostatic spray ionization (ESTASI) method to spray a portion of a sample droplet through a microhole in the cover plate, allowing its chemical content to be analyzed by MS. This eliminates the need for chip disassembly or the introduction of capillary emitters for MS analysis, as required by state-of-the-art. For the first time, this allows the essential advantage of a DMF device─free droplet movement─to be retained during MS analysis. The broad applicability of the developed seamless coupling of DMF and mass spectrometry was successfully applied to the study of various on-chip organic syntheses as well as protein and peptide analysis. In the case of a Hantzsch synthesis, we were able to show that the method is very well suited for monitoring even rapid chemical reactions that are completed in a few seconds. In addition, the strength of the low resource consumption in such on-chip microsyntheses was demonstrated by the example of enzymatic brominations, for which only a minute amount of a special haloperoxidase is required in the droplet. The unique selling point of this approach is that the analyzed droplet remains completely movable after the MS measurement and is available for subsequent on-DMF chip processes. This is illustrated here for the example of MS analysis of the starting materials in the corresponding droplets before they are combined to investigate the reaction progress by DMF-MS further. This technology enables the ongoing and almost unlimited tracking of multistep chemical processes in a DMF chip and offers exciting prospects for transforming digital microfluidics into automated synthesis platforms.

Complementary catalysis and analysis within solid state additively manufactured metal micro flow reactors

Additive Manufacturing is transforming how researchers and industrialists look to design and manufacture chemical devices to meet their specific needs. In this work, we report the first example of a flow reactor formed via the solid-state metal sheet lamination technique, Ultrasonic Additive Manufacturing (UAM), with directly integrated catalytic sections and sensing elements. The UAM technology not only overcomes many of the current limitations associated with the additive manufacturing of chemical reactionware but it also significantly increases the functionality of such devices. A range of biologically important 1, 4-disubstituted 1, 2, 3-triazole compounds were successfully synthesised and optimised in-flow through a Cu mediated Huisgen 1, 3-dipolar cycloaddition using the UAM chemical device. By exploiting the unique properties of UAM and continuous flow processing, the device was able to catalyse the proceeding reactions whilst also providing real-time feedback for reaction monitoring and optimisation.

Self-Optimization of Continuous Flow Electrochemical Synthesis Using Fourier Transform Infrared Spectroscopy and Gas Chromatography

A continuous-flow electrochemical synthesis platform has been developed to enable self-optimization of reaction conditions of organic electrochemical reactions using attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) and gas chromatography (GC) as online real-time monitoring techniques. We have overcome the challenges in using ATR FT-IR as the downstream analytical methods imposed when a large amount of hydrogen gas is produced from the counter electrode by designing two types of gas-liquid separators (GLS) for analysis of the product mixture flowing from the electrochemical reactor. In particular, we report an integrated GLS with an ATR FT-IR probe at the reactor outlet to give a facile and low-cost solution to determining the concentrations of products in gas-liquid two-phase flow. This approach provides a reliable method for quantifying low-volatile analytes, which can be problematic to be monitored by GC. Two electrochemical reactions the methoxylation of 1-formylpyrrolidine and the oxidation of 3-bromobenzyl alcohol were investigated to demonstrate that the optimal conditions can be located within the pre-defined multi-dimensional reaction parameter spaces without intervention of the operator by using the stable noisy optimization by branch and FIT (SNOBFIT) algorithm.

Developing flow photo-thiol–ene functionalizations of cinchona alkaloids with an autonomous self-optimizing flow reactor

Continuous flow photo-thiol–ene reactions on cinchona alkaloids with a variety of organic thiols have been developed using enabling technologies such as a self-optimizing flow photochemical reactor.

Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation

Application of artificial intelligence and machine learning for polymer discovery offers an opportunity to meet the drastic need for the next generation high performing and sustainable polymer materials. Here, these...

Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids

Research-scale fully automated flow platform for reaction self-optimization with solids handling facilitates identification of optimal conditions for continuous manufacturing of pharmaceuticals while reducing amounts of raw materials consumed.

Autonomous model-based experimental design for rapid reaction development

To automate and democratize model-based experimental design for flow chemistry applications, we report the development of open-source software, Optipus. Reaction models are built in an iterative and automated fashion, for rapid reaction development.

An integrated resource-efficient microfluidic device for parallelised studies of immobilised chiral catalysts in continuous flow via miniaturized LC/MS-analysis

Dual-μReactor catalysis screening: a novel method combining multiple miniaturized packed-bed reactors and on-line HPLC/MS-analysis on one single microfluidic device.

3D printed ceramics as solid supports for enzyme immobilization: an automated DoE approach for applications in continuous flow

In recent years, 3D printing has emerged in the field of chemical engineering as a powerful manufacturing technique to rapidly design and produce tailor-made reaction equipment. In fact, reactors with complex internal geometries can be easily fabricated, optimized and interchanged in order to respond to precise process needs, such as improved mixing and increased surface area. These advantages make them interesting especially for catalytic applications, since customized structured bed reactors can be easily produced. 3D printing applications are not limited to reactor design, it is also possible to realize functional low cost alternatives to analytical equipment that can be used to increase the level of process understanding while keeping the investment costs low. In this work, in-house designed ceramic structured inserts printed via vat photopolymerization (VPP) are presented and characterized. The flow behavior inside these inserts was determined with residence time distribution (RTD) experiments enabled by in-house designed and 3D printed inline photometric flow cells. As a proof of concept, these structured inserts were fitted in an HPLC column to serve as solid inorganic supports for the immobilization of the enzyme Phenolic acid Decarboxylase (bsPAD), which catalyzes the decarboxylation of cinnamic acids. The conversion of coumaric acid to vinylphenol was chosen as a model system to prove the implementation of these engineered inserts in a continuous biocatalytic application with high product yield and process stability. The setup was further automated in order to quickly identify the optimum operating conditions via a Design of Experiments (DoE) approach. The use of a systematic optimization, together with the adaptability of 3D printed equipment to the process requirements, render the presented approach highly promising for a more feasible implementation of biocatalysts in continuous industrial processes. Graphical abstract.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41981-021-00163-4.

Sampling and Analysis in Flow: The Keys to Smarter, More Controllable, and Sustainable Fine-Chemical Manufacturing

Process analytical technology (PAT) is a system designed to help chemists better understand and control manufacturing processes. PAT systems operate through the combination of analytical devices, reactor control elements, and mathematical models to ensure the quality of the final product through a quality by design (QbD) approach. The expansion of continuous manufacturing in the pharmaceutical and fine-chemical industry requires the development of PAT tools suitable for continuous operation in the environment of flow reactors. This requires innovative approaches to sampling and analysis from flowing media to maintain the integrity of the reactor content and the analyte of interest. The following Review discusses examples of PAT tools implemented in flow chemistry for the preparation of small organic molecules, and applications of self-optimization tools.

High-Throughput and Combinatorial Approaches for the Development of Multifunctional Polymers

High-throughput (HT) development of new multifunctional polymers is accomplished by the combination of different HT tools established in polymer sciences in the last decade. Important advances are robotic/HT synthesis of polymer libraries, the HT characterization of polymers, and the application of spatially resolved polymer library formats, explicitly microarray and gradient libraries. HT polymer synthesis enables the generation of material libraries with combinatorial design motifs. Polymer composition, molecular weight, macromolecular architecture, etc. may be varied in a systematic, fine-graded manner to obtain libraries with high chemical diversity and sufficient compositional resolution as model systems for the screening of these materials for the functions aimed. HT characterization allows a fast assessment of complementary properties, which are employed to decipher quantitative structure-properties relationships. Moreover, these methods facilitate the HT determination of important surface parameters by spatially resolved characterization methods, including time-of-flight secondary ion mass spectrometry and X-ray photoelectron spectroscopy. Here current methods for the high-throughput robotic synthesis of multifunctional polymers as well as their characterization are presented and advantages as well as present limitations are discussed.

Data-science driven autonomous process optimization

Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.

The Longer Route can be Better: Electrosynthesis in Extended Path Flow Cells

This personal account provides an overview of work conducted in my research group, and through collaborations with other chemists and engineers, to develop flow electrolysis cells and apply these cells in organic electrosynthesis. First, a brief summary of my training and background in organic synthesis is provided, leading in to the start of flow electrosynthesis in my lab in collaboration with Derek Pletcher. Our work on the development of extended path electrolysis flow reactors is described from a synthetic organic chemist's perspective, including laboratory scale-up to give several moles of an anodic methoxylation product in one day. The importance of cell design is emphasised with regards to achieving good performance in laboratory electrosynthesis with productivities from hundreds of mg h^-1 to many g h^-1 , at high conversion in a selective fashion. A simple design of recycle flow cell that can be readily constructed in a small University workshop is also discussed, including simple modifications to improve cell performance. Some examples of flow electrosyntheses are provided, including Shono-type oxidation, anodic cleavage of protecting groups, Hofer-Moest reaction of cubane carboxylic acids, oxidative esterification and amidation of aldehydes, and reduction of aryl halides.

Simultaneous self-optimisation of yield and purity through successive combination of inline FT-IR spectroscopy and online mass spectrometry in flow reactions

Self-optimisation constitutes a very helpful tool for chemical process development, both in lab and in industrial applications. However, research on the application of model-free autonomous optimisation strategies (based on experimental investigation) for complex reactions of high industrial significance, which involve considerable intermediate and by-product formation, is still in an early stage. This article describes the development of an enhanced autonomous microfluidic reactor platform for organolithium and epoxide reactions that incorporates a successive combination of inline FT-IR spectrometer and online mass spectrometer. Experimental data is collected in real-time and used as feedback for the optimisation algorithms (modified Simplex algorithm and Design of Experiments) without time delay. An efficient approach to handle intricate optimisation problems is presented, where the inline FT-IR measurements are used to monitor the reaction’s main components, whereas the mass spectrometer’s high sensitivity permits insights into the formation of by-products. To demonstrate the platform’s flexibility, optimal reaction conditions of two organic syntheses are identified. Both pose several challenges, as complex reaction mechanisms are involved, leading to a large number of variable parameters, and a considerable amount of by-products is generated under non-ideal process conditions. Through multidimensional real-time optimisation, the platform supersedes labor- and cost-intensive work-up procedures, while diminishing waste generation, too. Thus, it renders production processes more efficient and contributes to their overall sustainability.

Graphical abstract

An automated computational approach to kinetic model discrimination and parameter estimation

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc.) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification.

Advanced Real-Time Process Analytics for Multistep Synthesis in Continuous Flow*

In multistep continuous flow chemistry, studying complex reaction mixtures in real time is a significant challenge, but provides an opportunity to enhance reaction understanding and control. We report the integration of four complementary process analytical technology tools (NMR, UV/Vis, IR and UHPLC) in the multistep synthesis of an active pharmaceutical ingredient, mesalazine. This synthetic route exploits flow processing for nitration, high temperature hydrolysis and hydrogenation reactions, as well as three inline separations. Advanced data analysis models were developed (indirect hard modeling, deep learning and partial least squares regression), to quantify the desired products, intermediates and impurities in real time, at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating both steady state and dynamic experiments and represents a significant step forward in data-driven continuous flow synthesis.

Automated Experimentation Powers Data Science in Chemistry

Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.

In situ sensors for flow reactors – a review

A comprehensive review on integrating microfluidic reactors with in situ sensors for reaction probing of chemical transformation.

Digitising chemical synthesis in automated and robotic flow

Continuous flow chemical synthesis is already known to have many attributes that give it superiority over batch processes in several respects. To expand these advantages with those from automation will only drive such enabling technologies further into the faster producing, more efficient 21st century chemical world. In this report we present several examples of algorithmic chemical search, along with flow platforms that link hardware and digital chemical operations on software. This enables organic syntheses to be automatically carried out and optimised with as little human intervention as possible. By applying such enabling technologies to the production of small organic molecules and pharmaceutical compounds in end-to-end multistep processes, a range of reaction types can be accessed and, thus, the flexibility of these single, compact flow designs may be revealed. Automated systems can allow several reactions to take place on the same setup, enabling direct comparison of reactions under different conditions. Moreover, the production of new and known target compounds can be made faster and more efficient, the recipes of which can then be stored as digital files. Some of the automating software has employed machine-powered learning to assist the chemist in developing intelligent algorithms and artificial intelligence (AI) driven synthetic route planning. This ultimately produces a continuous flow platform that can design its own viable pathway to a particular molecule and then carry it out on its own, allowing the chemists, at the same time, to apply their expertise to other pressing challenges in their fields.

High-Throughput Approaches for the Discovery of Supramolecular Organic Cages

The assembly of complex molecules, such as organic cages, can be achieved through supramolecular and dynamic covalent strategies. Their use in a range of applications has been demonstrated, including gas uptake, molecular separations, and in catalysis. However, the targeted design and synthesis of new species for particular applications is challenging, particularly as the systems become more complex. High-throughput computation-only and experiment-only approaches have been developed to streamline the discovery process, although are still not widely implemented. Additionally, combined hybrid workflows can dramatically accelerate the discovery process and lead to the serendipitous discovery and rationalisation of new supramolecular assemblies that would not have been designed based on intuition alone. This Minireview focuses on the advances in high-throughput approaches that have been developed and applied in the discovery of supramolecular organic cages.

The Concept of Chemical Generators: On-Site On-Demand Production of Hazardous Reagents in Continuous Flow

In recent years, a steadily growing number of chemists, from both academia and industry, have dedicated their research to the development of continuous flow processes performed in milli- or microreactors. The common availability of continuous flow equipment at virtually all scales and affordable cost has additionally impacted this trend. Furthermore, regulatory agencies such as the United States Food and Drug Administration actively encourage continuous manufacturing of active pharmaceutical ingredients (APIs) with the vision of quality and productivity improvements. That is why the pharmaceutical industry is progressively implementing continuous flow technologies. As a result of the exceptional characteristics of continuous flow reactors such as small reactor volumes and remarkably fast heat and mass transfer, process conditions which need to be avoided in conventional batch syntheses can be safely employed. Thus, continuous operation is particularly advantageous for reactions at high temperatures/pressures (novel process windows) and for ultrafast, exothermic reactions (flash chemistry).

In addition to conditions that are outside of the operation range of conventional stirred tank reactors, reagents possessing a high hazard potential and therefore not amenable to batch processing can be safely utilized (forbidden chemistry). Because of the small reactor volumes, risks in case of a failure are minimized. Such hazardous reagents often are low molecular weight compounds, leading generally to the most atom-, time-, and cost-efficient route toward the desired product. Ideally, they are generated from benign, readily available and cheap precursors within the closed environment of the flow reactor on-site on-demand. By doing so, the transport, storage, and handling of those compounds, which impose a certain safety risk especially on a large scale, are circumvented. This strategy also positively impacts the global supply chain dependency, which can be a severe issue, particularly in times of stricter safety regulations or an epidemic. The concept of the in situ production of a hazardous material is generally referred to as the “generator” of the material. Importantly, in an integrated flow process, multiple modules can be assembled consecutively, allowing not only an in-line purification/separation and quenching of the reagent, but also its downstream conversion to a nonhazardous product.

For the past decade, research in our group has focused on the continuous generation of hazardous reagents using a range of reactor designs and experimental techniques, particularly toward the synthesis of APIs. In this Account, we therefore introduce chemical generator concepts that have been developed in our laboratories for the production of toxic, explosive, and short-lived reagents. We have defined three different classes of generators depending on the reactivity/stability of the reagents, featuring reagents such as Br₂, HCN, peracids, diazomethane (CH₂N₂), or hydrazoic acid (HN₃). The various reactor designs, including in-line membrane separation techniques and real-time process analytical technologies for the generation, purification, and monitoring of those hazardous reagents, and also their downstream transformations are presented. This Account should serve as food for thought to extend the scope of chemical generators for accomplishing more efficient and more economic processes.

Continuous Flow Upgrading of Selected C2-C6 Platform Chemicals Derived from Biomass

The ever increasing industrial production of commodity and specialty chemicals inexorably depletes the finite primary fossil resources available on Earth. The forecast of population growth over the next 3 decades is a very strong incentive for the identification of alternative primary resources other than petro-based ones. In contrast with fossil resources, renewable biomass is a virtually inexhaustible reservoir of chemical building blocks. Shifting the current industrial paradigm from almost exclusively petro-based resources to alternative bio-based raw materials requires more than vibrant political messages; it requires a profound revision of the concepts and technologies on which industrial chemical processes rely. Only a small fraction of molecules extracted from biomass bears significant chemical and commercial potentials to be considered as ubiquitous chemical platforms upon which a new, bio-based industry can thrive. Owing to its inherent assets in terms of unique process experience, scalability, and reduced environmental footprint, flow chemistry arguably has a major role to play in this context. This review covers a selection of C₂ to C₆ bio-based chemical platforms with existing commercial markets including polyols (ethylene glycol, 1,2-propanediol, 1,3-propanediol, glycerol, 1,4-butanediol, xylitol, and sorbitol), furanoids (furfural and 5-hydroxymethylfurfural) and carboxylic acids (lactic acid, succinic acid, fumaric acid, malic acid, itaconic acid, and levulinic acid). The aim of this review is to illustrate the various aspects of upgrading bio-based platform molecules toward commodity or specialty chemicals using new process concepts that fall under the umbrella of continuous flow technology and that could change the future perspectives of biorefineries.

Disposable cartridge concept for the on-demand synthesis of turbo Grignards, Knochel-Hauser amides, and magnesium alkoxides

Magnesium organometallic reagents occupy a central position in organic synthesis. The freshness of these compounds is the key for achieving a high conversion and reproducible results. Common methods for the synthesis of Grignard reagents from metallic magnesium present safety issues and exhibit a batch-to-batch variability. Tubular reactors of solid reagents combined with solution-phase reagents enable the continuous-flow preparation of organomagnesium reagents. The use of stratified packed-bed columns of magnesium metal and lithium chloride for the synthesis of highly concentrated turbo Grignards is reported. A low-cost pod-style synthesizer prototype, which incorporates single-use prepacked perfluorinated cartridges and bags of reagents for the automated on-demand lab-scale synthesis of carbon, nitrogen, and oxygen turbo magnesium bases is presented. This concept will provide access to fresh organomagnesium reagents on a discovery scale and will do so independent from the operator’s experience in flow and/or organometallic chemistry.

An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge

We present a chemical discovery robot for the efficient and reliable discovery of supramolecular architectures through the exploration of a huge reaction space exceeding ten billion combinations. The system was designed to search for areas of reactivity found through autonomous selection of the reagent types, amounts, and reaction conditions aiming for combinations that are reactive. The process consists of two parts where reagents are mixed together, choosing from one type of aldehyde, one amine and one azide (from a possible family of two amines, two aldehydes and four azides) with different volumes, ratios, reaction times, and temperatures, whereby the reagents are passed through a copper coil reactor. Next, either cobalt or iron is added, again from a large number of possible quantities. The reactivity was determined by evaluating differences in pH, UV‐Vis, and mass spectra before and after the search was started. The algorithm was focused on the exploration of interesting regions, as defined by the outputs from the sensors, and this led to the discovery of a range of 1‐benzyl‐(1,2,3‐triazol‐4‐yl)‐N‐alkyl‐(2‐pyridinemethanimine) ligands and new complexes: [Fe(L¹)₂](ClO₄)₂ (1); [Fe(L²)₂](ClO₄)₂ (2); [Co₂(L³)₂](ClO₄)₄ (3); [Fe₂(L³)₂](ClO₄)₄ (4), which were crystallised and their structure confirmed by single‐crystal X‐ray diffraction determination, as well as a range of new supramolecular clusters discovered in solution using high‐resolution mass spectrometry.

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.