Dissecting diazirine photo-reaction mechanism for protein residue-specific cross-linking and distance mapping

While photo-cross-linking (PXL) with alkyl diazirines can provide stringent distance restraints and offer insights into protein structures, unambiguous identification of cross-linked residues hinders data interpretation to the same level that has been achieved with chemical cross-linking (CXL). We address this challenge by developing an in-line system with systematic modulation of light intensity and irradiation time, which allows for a quantitative evaluation of diazirine photolysis and photo-reaction mechanism. Our results reveal a two-step pathway with mainly sequential generation of diazo and carbene intermediates. Diazo intermediate preferentially targets buried polar residues, many of which are inaccessible with known CXL probes for their limited reactivity. Moreover, we demonstrate that tuning light intensity and duration enhances selectivity towards polar residues by biasing diazo-mediated cross-linking reactions over carbene ones. This mechanistic dissection unlocks the full potential of PXL, paving the way for accurate distance mapping against protein structures and ultimately, unveiling protein dynamic behaviors.

In situ Diazonium Salt Formation and Photochemical Aryl-Aryl Coupling in Continuous Flow Monitored by Inline NMR Spectroscopy

A novel class of diazonium salts is introduced for the photochemical aryl-aryl coupling to produce (substituted) biphenyls. As common diazonium tetrafluoroborate salts fail, soluble and safe aryl diazonium trifluoroacetates are applied. In this mild synthesis route no catalysts are required to generate an aryl-radical by irradiation with UV-A light (365 nm). This reactive species undergoes direct C-H arylation at an arene, forming the product in reasonable reaction times. With the implementation of a continuous flow setup in a capillary photoreactor 13 different biphenyl derivatives are successfully synthesized. By integrating an inline 19F-NMR benchtop spectrometer, samples are reliably quantified as the fluorine-substituents act as a probe. Here, real-time NMR spectroscopy is a perfect tool to monitor the continuously operated system, which produces fine chemicals of industrial relevance even in a multigram scale.

Dynamic sampling in autonomous process optimization

Autonomous process optimization (APO) is a technology that has recently found utility in a multitude of process optimization challenges. In contrast to most APO examples in microflow reactor systems, we recently presented a system capable of optimization in high-throughput batch reactor systems. The drawback of APO in a high-throughput batch reactor system is the reliance on reaction sampling at a predetermined static timepoint rather than a dynamic endpoint. Static timepoint sampling can lead to the inconsistent capture of the process performance under each process parameter permutation. This is important because critical process behaviors such as rate acceleration accompanied by decomposition could be missed entirely. To address this drawback, we implemented a dynamic reaction endpoint determination strategy to capture the product purity once the process stream stabilized. We accomplished this through the incorporation of a real-time plateau detection algorithm into the APO workflow to measure and report the product purity at the dynamically determined reaction endpoint. We then applied this strategy to the autonomous optimization of a photobromination reaction towards the synthesis of a pharmaceutically relevant intermediate. In doing so, we not only uncovered process conditions to access the desired monohalogenation product in 85 UPLC area % purity with minimal decomposition risk, but also measured the effect of each parameter on the process performance. Our results highlight the advantage of incorporating dynamic sampling in APO workflows to drive optimization toward a stable and high-performing process.

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.

Self-driving laboratories: A paradigm shift in nanomedicine development

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial, in part due to the complexity associated with their preclinical development. Herein, we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next-generation nanomedicines but also encourage participation of the pharmaceutical science community in a large data curation initiative.

Autonomous Multi-Step and Multi-Objective Optimization Facilitated by Real-Time Process Analytics

Autonomous flow reactors are becoming increasingly utilized in the synthesis of organic compounds, yet the complexity of the chemical reactions and analytical methods remains limited. The development of a modular platform which uses rapid flow NMR and FTIR measurements, combined with chemometric modeling, is presented for efficient and timely analysis of reaction outcomes. This platform is tested with a four variable single-step reaction (nucleophilic aromatic substitution), to determine the most effective optimization methodology. The self-optimization approach with minimal background knowledge proves to provide the optimal reaction parameters within the shortest operational time. The chosen approach is then applied to a seven variable two-step optimization problem (imine formation and cyclization), for the synthesis of the active pharmaceutical ingredient edaravone. Despite the exponentially increased complexity of this optimization problem, the platform achieves excellent results in a relatively small number of iterations, leading to >95% solution yield of the intermediate and up to 5.42 kg L^-1 h^-1 space-time yield for this pharmaceutically relevant product.

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 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.

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.

Chemputation and the Standardization of Chemical Informatics

The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture-the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation-that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.

Automated High-Pressure Atline Analysis of Photo-High-P,T Vitamin D3 Microfluidic Synthesis

Process analytical technology has become a relevant topic in both industry and academia as a mechanism to control process quality by measuring critical parameters; being mainly applied in pharmaceutical industry. An emerging topic is process monitoring with subsequent process automation in flow chemistry using inline, online and atline analyzers. Flow chemistry often deliberately and favorably uses harsh conditions (termed Novel Process Windows) to achieve process intensification which raises the need for sampling under these conditions. This demands for setting in place a stabilization of the sample before exposing it to the processing. Ignoring this may result in being unable to use inline/online analytics and posing the need for a separation step before quantitative analysis, leaving atline analysis as the only feasible option. That means that sampling and connected operations need also to be automated. This is where this study sets in, and this is enabled by a modified high-performance liquid chromatography (HPLC) autosampler coupled to the photo-high-p,T flow synthesis of vitamin D3. It shows that sampling variables, such as decompression speed, can be even more critical in terms of variability of results than process variables such as concentration, pressure, and temperature. The modification enabled the autosampler fully automated and unattended sampling from the reactor and enabled pressure independent measurements with 89% accuracy, >95% reproducibility, and >96% repeatability, stating decompression speed as the primary responsibility for measurements’ uncertainty.

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.

Continuous Flow Synthesis of [Au(NHC)(Aryl)] (NHC=N-Heterocyclic Carbene) Complexes

The use of weak and inexpensive bases has recently opened promising perspectives towards the simpler and more sustainable synthesis of Au(I)-aryl complexes with valuable applications in catalysis, medicinal chemistry, and materials science. In recent years, continuous manufacturing has shown to be a reliable partner in establishing sustainable and controlled process scalability. Herein, the first continuous flow synthesis of a range of Au(I)-aryl starting from widely available boronic acids and various [Au(NHC)Cl] (NHC=N-heterocyclic carbene) complexes in unprecedentedly short reaction times and high yields is reported. Successful synthesis of previously non- or poorly accessible complexes exposed fascinating reactivity patterns. Via a gram-scale synthesis, convenient process scalability of the developed protocol was showcased.

Automated and enabling technologies for medicinal chemistry

Having always been driven by the need to get new treatments to patients as quickly as possible, drug discovery is a constantly evolving process. This chapter will review how medicinal chemistry was established, how it has changed over the years due to the emergence of new enabling technologies, and how early advances in synthesis, purification and analysis, have provided the foundations upon which the current automated and enabling technologies are built. Looking beyond the established technologies, this chapter will also consider technologies that are now emerging, and their impact on the future of drug discovery and the role of medicinal chemists.

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

Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions

Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI's role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories' self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials' synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.

Recent Advances in Continuous-Flow Reactions Using Metal-Free Homogeneous Catalysts

Developments that result in high-yielding, low-cost, safe, scalable, and less-wasteful processes are the most important goals in synthetic organic chemistry. Continuous-flow reactions have garnered much attention due to many advantages over conventional batch reactions that include precise control of short reaction times and temperatures, low risk in handling dangerous compounds, and ease in scaling up synthesis. Combinations of continuous-flow reactions with homogeneous, metal-free catalysts further enhances advantages that include low-cost and ready availability, low toxicity, higher stability in air and water, and increased synthetic efficiency due to the avoidance of the time-consuming removal of toxic metal traces. This review summarizes recently reported continuous-flow reactions using metal-free homogeneous catalysts and classifies them either as acidic catalysts, basic catalysts, or miscellaneous catalysts. In addition, we compare the results between continuous-flow conditions and conventional batch conditions to reveal the advantages of using flow reactions with metal-free homogeneous catalysts.

A Visible-Light-Powered Polymerization Method for the Immobilization of Enantioselective Organocatalysts into Microreactors

A versatile one‐step photopolymerization approach for the immobilization of enantioselective organocatalysts is presented. Chiral organocatalyst‐containing monoliths based on polystyrene divinylbenzene copolymer were generated inside channels of microfluidic chips. Exemplary performance tests were performed for the monolithic Hayashi–Jørgensen catalyst in continuous flow, which showed good results for the Michael addition of aldehydes to nitroalkenes in terms of stereoselectivity and catalyst stability with minimal consumption of reagents and solvents.

Memory of chirality in a room temperature flow electrochemical reactor

Chiral compounds have become of great interest to the pharmaceutical industry as they possess various biological activities. Concurrently, the concept of “memory of chirality” has been proven as a powerful tool in asymmetric synthesis, while flow chemistry has begun its rise as a new enabling technology to add to the ever increasing arsenal of techniques available to the modern day chemist. Here, we have employed a new simple electrochemical microreactor design to oxidise an l-proline derivative at room temperature in continuous flow. Compared to batch, organic electrosynthesis via microflow reactors are advantageous because they allow shorter reaction times, optimization and scale up, safer working environments, and high selectivities (e.g. reduce overoxidation). Flow electrochemical reactors also provide high surface-to-volume ratios and impart the possibility of excluding the supporting electrolyte due to a very short interelectrode distance. By the comparison of Hofer Moest type electrochemical oxidations at room temperature in batch and flow, we conclude that continuous flow electrolysis is superior to batch, producing a good yield (71%) and a higher enantiomeric excess (64%). These results show that continuous flow has the potential to act as a new enabling technology for asymmetric synthesis to replace some aspects of conventional batch electrochemical processes.

Automated synthesis: current platforms and further needs

Organic synthesis is a vital process that is a mainstay of drug discovery. However, traditional manual-based approaches to organic synthesis might not be economical, especially in a research environment where budgets are increasingly restricted and the effective use of manpower and materials is crucial. Hence, there is a strong interest in automating the synthesis process, resulting in a growth in synthesis automation, especially of systems and configuration. Here, we systematically summarize recently developed automated systems for organic synthesis. This review will be useful for computational scientists aiming to develop novel tools and also for non-specialists and students to understand the frontier of automated synthesis.

The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology

Medicinal chemistry plays a fundamental and underlying role in chemical biology, pharmacology, and medicine to discover safe and efficacious drugs. Small molecule medicinal chemistry relies on iterative learning cycles composed of compound design, synthesis, testing, and data analysis to provide new chemical probes and lead compounds for novel and druggable targets. Using traditional approaches, the time from hypothesis to obtaining the results can be protracted, thus limiting the number of compounds that can be advanced into clinical studies. This challenge can be tackled with the recourse of enabling technologies that are showing great potential in improving the drug discovery process. In this Perspective, we highlight recent developments toward innovative medicinal chemistry strategies based on continuous flow systems coupled with automation and bioassays. After a discussion of the aims and concepts, we describe equipment and representative examples of automated flow systems and end-to-end prototypes realized to expedite medicinal chemistry discovery cycles.

One Amine-3 Tasks: Reductive Coupling of Imines with Olefins in Batch and Flow

Owing to their wide range of biological properties, γ-aminobutyric acid derivatives (GABA) have been extensively studied and found noteworthy industrial applications. However, atom-economical and efficient processes for their production are scarce and would greatly benefit from further investigations. Herein, we demonstrate that an iridium-based photocatalyst promotes the direct reductive cross-coupling of imines with olefins upon irradiation with visible light to give GABA derivatives in good yields and selectivities. We also stress the enabling triple role of tributylamine additive in this process, discuss the advantages of strategies based on proton-coupled electron transfer (PCET) and demonstrate the scale-up of this reaction in continuous flow.

Self-optimising processes and real-time-optimisation of organic syntheses in a microreactor system using Nelder–Mead and design of experiments

Comparing an enhanced simplex algorithm with model-free design of experiments, this work presents a flexible platform for multi-objective, real-time optimisation.

Multivariate analysis of inline benchtop NMR data enables rapid optimization of a complex nitration in flow

Multivariate analysis is applied to inline benchtop NMR data for a complex nitration in flow. This rapid quantification enables reaction optimization using advanced techniques in flow, such as design of experiments and dynamic experimentation.

In-situ time resolved spectrographic measurement using an additively manufactured metallic micro-fluidic analysis platform

Introduction

Microfluidic reactionware allows small volumes of reagents to be utilized for highly controlled flow chemistry applications. By integrating these microreactors with onboard analytical systems, the devices change from passive ones to active ones, increasing their functionality and usefulness. A pressing application for these active microreactors is the monitoring of reaction progress and intermediaries with respect to time, shedding light on important information about these real-time synthetic processes.

Objective

In this multi-disciplinary study the objective was to utilise advanced digital fabrication to research metallic, active microreactors with integrated fibre optics for reaction progress monitoring of solvent based liquids, incompatible with previously researched polymer devices, in combination with on-board Ultraviolet-visible spectroscopy for real-time reaction monitoring.

Method

A solid-state, metal-based additive manufactured system (Ultrasonic Additive Manufacturing) combined with focussed ion beam milling, that permitted the accurate embedment of delicate sensory elements directly at the point of need within aluminium layers, was researched as a method to create active, metallic, flow reactors with on-board sensing. This outcome was then used to characterise and correctly identify concentrations of UV-active water-soluble B-vitamin nicotinamide and fluorescein. A dilution series was formed from 0.01–1.75 mM; which was pumped through the research device and monitored using UV-vis spectroscopy.

Results

The results uniquely showed the in-situ ion milling of ultrasonically embedded optical fibres resulted in a metallic microfluidic reaction and monitoring device capable of measuring solvent solutions from 18 μM to 18 mM of nicotinamide and fluorescein, in real time. This level of accuracy highlights that the researched device and methods are capable of real-time spectrographic analysis of a range of chemical reactions outside of those possible with polymer devices.

Automated Polymer Synthesis Platform for Integrated Conversion Targeting Based on Inline Benchtop NMR

An automated polymer synthesis platform based on an inline low-field nuclear magnetic resonance spectrometer is developed. Flow chemistry and automated inline analyses are an excellent combination for automated kinetic screening and for self-optimizing reactions with programmable conversion targeting. By monitoring monomer conversion over a continuous range of reactor residence times, the platform is able to construct kinetic profiles of polymerizations in an accurate and efficient way. The machine-assisted self-optimization routine allows the reaction to be stopped at any given preselected conversion, giving rise to unprecedented reproducibility in polymer synthesis.

Reconfigurable Flow Platform for Automated Reagent Screening and Autonomous Optimization for Bioinspired Lignans Synthesis

Naturally occurring benzoxanthenones, which belong to the vast family of lignans, are promising biologically relevant targets. They are biosynthetically produced by the oxidative dimerization of 2-propenyl phenols. In this manuscript, we disclose a powerful automated flow-based strategy for identifying and optimizing a cobalt-catalyzed oxidizing system for the bioinspired dimerization of 2-propenyl phenols. We designed a reconfigurable flow reactor associating online monitoring and process control instrumentation. Our machine was first configured as an automated screening platform to evaluate a matrix of 4 catalysts (plus the blank) and 5 oxidants (plus the blank) at two different temperatures, resulting in an array of 50 reactions. The automated screening was conducted on micromole scale at a rate of one fully characterized reaction every 26 min. After having identified the most promising cobalt-catalyzed oxidizing system, the automated screening platform was straightforwardly reconfigured to an autonomous self-optimizing flow reactor by implementation of an optimization algorithm in the closed-loop system. The optimization campaign allowed the determination of very effective experimental conditions in a limited number of experiments, which allowed us to prepare the natural products carpanone and polemannone B as well as synthetic analogues.