Optimizing the Heck–Matsuda Reaction in Flow with a Constraint-Adapted Direct Search Algorithm

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

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.

Will the next generation of chemical plants be in miniaturized flow reactors?

For decades, a production paradigm based on centralized, stepwise, large scale processes has dominated the chemical industry horizon. While effective to meet an ever increasing demand for high value-added chemicals, the so-called macroscopic batch reactors are also associated with inherent weaknesses and threats; some of the most obvious ones were tragically illustrated over the past decades with major industrial disasters and impactful disruptions of advanced chemical supplies. The COVID pandemic has further emphasized that a change in paradigm was necessary to sustain chemical production with an increased safety, reliable supply chains and adaptable productivities. More than a decade of research and technology development has led to alternative and effective chemical processes relying on miniaturised flow reactors (a.k.a. micro and mesofluidic reactors). Such miniaturised reactors bear the potential to solve safety concerns and to improve the reliability of chemical supply chains. Will they initiate a new paradigm for a more localized, safe and reliable chemical production?

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.

An Integrated Method of Bayesian Optimization and D-Optimal Design for Chemical Experiment Optimization

The smart chemical laboratory has recently emerged as a promising trend for future chemical research, where experiment optimization is of vital importance. The traditional Bayesian optimization (BO) algorithm focuses on exploring the dependent variable space while overlooking the independent variable space. Consequently, the BO algorithm suffers from becoming stuck at local optima, which severely deteriorates the optimization performance, especially with bad-quality initial points. Herein, we propose a novel stochastic framework of Bayesian optimization with D-optimal design (BODO) by integrating BO with D-optimal design. BODO can balance the exploitation in the dependent variable space and the exploration in the independent variable space. We highlight the excellent performance of BODO even with poor initial points on the benchmark alpine2 function. Meanwhile, BODO demonstrates a better average objective function value than BO on the benchmark Summit SnAr chemical process, showing its advantage in chemical experiment optimization and potential application in future chemical experiments.

Online monitoring of small volume reactions using compact liquid chromatography instrumentation

A wide variety of analytical techniques have been employed for monitoring chemical reactions, with online instrumentation providing additional benefits compared to offline analysis. A challenge in the past for online monitoring has been placement of the monitoring instrumentation as close as possible to the reaction vessel to maximize sampling temporal resolution and preserve sample composition integrity. Furthermore, the ability to sample very small volumes from bench-scale reactions allows the use of small reaction vessels and conservation of expensive reagents. In this study, a compact capillary LC instrument was used for online monitoring of as small as 1 mL total volume of a chemical reaction mixture, with automated sampling of nL-scale volumes directly from the reaction vessel used for analysis. Analyses to demonstrate short term (~2 h) and long term (~ 50 h) reactions were conducted using tandem on-capillary ultraviolet absorbance followed by in-line MS detection or ultraviolet absorbance detection alone, respectively. For both short term and long term reactions (10 and 250 injections, respectively), sampling approaches using syringe pumps minimized the overall sample loss to ~0.2% of the total reaction volume.

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.

Revisiting aromatic diazotization and aryl diazonium salts in continuous flow: highlighted research during 2001–2021

Aryl diazonium salts play an important role in chemical transformations; however their explosive nature limits their applications in batch.

Rxn Rover: automation of chemical reactions with user-friendly, modular software

Automation of chemical reactions through tools such as Rxn Rover in research and development is an enabling technology to reduce cost and waste management in technology transformations towards renewable feedstocks and energy in the chemical industry.

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.

Forgotten and forbidden chemical reactions revitalised through continuous flow technology

Continuous flow technology has played an undeniable role in enabling modern chemical synthesis, whereby a myriad of reactions can now be performed with greater efficiency, safety and control. As flow chemistry furthermore delivers more sustainable and readily scalable routes to important target structures a growing number of industrial applications are being reported. In this review we highlight the impact of flow chemistry on revitalising important chemical reactions that were either forgotten soon after their initial report as necessary improvements were not realised due to a lack of available technology, or forbidden due to unacceptable safety concerns relating to the experimental procedure. In both cases flow processing in combination with further reaction optimisation has rendered a powerful set of tools that make such transformations not only highly efficient but moreover very desirable due to a more streamlined construction of desired scaffolds. This short review highlights important contributions from academic and industrial laboratories predominantly from the last 5 years allowing the reader to gain an appreciation of the impact of flow chemistry.

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.

Stereoselective Remote Functionalization via Palladium-Catalyzed Redox-Relay Heck Methodologies

Exploration of novel, three-dimensional chemical space is of growing interest in the drug discovery community and with this comes the challenge for synthetic chemists to devise new stereoselective methods to introduce chirality in a rapid and efficient manner. This Minireview provides a timely summary of the development of palladium-catalyzed asymmetric redox-relay Heck-type processes. These reactions represent an important class of transformation for the selective introduction of remote stereocenters, and have risen to prominence over the past decade. Within this Minireview, the vast scope of these transformations will be showcased, alongside applications to pharmaceutically relevant chiral building blocks and drug substances. To complement this overview, a mechanistic summary and discussion of the current limitations of the transformation are presented, followed by an outlook on future areas of investigation.

Development of a continuous flow synthesis of FGIN-1-27 enabled by in-line 19F NMR analyses and optimization algorithms

A continuous flow synthesis of FGIN-1-27 has been developed using enabling technologies such as real-time in-line benchtop 19F NMR analysis and an optimization algorithm.

Artificial intelligence and automation in computer aided synthesis planning

In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.

Recent Applications of Continuous Flow in Homogeneous Palladium Catalysis

Considerable advances have been made using continuous flow chemistry as an enabling tool in organic synthesis. Consequently, the number of articles reporting continuous flow methods has increased significantly in recent years. This review covers the progress achieved in homogeneous palladium catalysis using continuous flow conditions over the last five years, including C–C/C–N cross-coupling reactions, carbonylations and reductive/oxidative transformations.

1 Introduction

2 C–C Cross-Coupling Reactions

3 C–N Coupling Reactions

4 Carbonylation Reactions

5 Miscellaneous Reactions

6 Key to Schematic Symbols

7 Conclusion

Self-optimising reactive extractions: towards the efficient development of multi-step continuous flow processes

Downstream purification of products and intermediates is essential for the development of continuous flow processes. Described herein, is a study on the use of a modular and reconfigurable continuous flow platform for the self-optimisation of reactive extractions and multi-step reaction-extraction processes. The selective extraction of one amine from a mixture of two similar amines was achieved with an optimum separation of 90%, and in this case, the black-box optimisation approach was superior to global polynomial modelling. Furthermore, this methodology was utilised to simultaneously optimise the continuous flow synthesis and work-up of N-benzyl-α-methylbenzylamine with respect to four variables, resulting in a significantly improved purity.

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.

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

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.

An autonomous microreactor platform for the rapid identification of kinetic models

Rapid estimation of kinetic parameters with high precision is facilitated by automation combined with online Model-Based Design of Experiments.

Algorithms for the self-optimisation of chemical reactions

Self-optimising chemical systems have experienced a growing momentum in recent years. Herein, we review algorithms used for the self-optimisation of chemical reactions in an accessible way for the general chemist.

Automated platforms for reaction self-optimization in flow

This article highlights recent progress in continuous flow self-optimizing platforms.

Practical and Scalable Organic Reactions with Flow Microwave Apparatus

Microwave irradiation has been used for accelerating organic reactions as a heating method and has been proven to be useful in laboratory scale organic synthesis. The major drawback of microwave chemistry is the difficulty in scaling up, mainly because of the low penetration depth of microwaves. The combination of microwave chemistry and flow chemistry is considered to overcome the problem in scaling up of microwave-assisted organic reactions, and some flow microwave systems have been developed in both academic and industrial communities. In this context, we have demonstrated the scale-up of fundamental organic reactions using a novel flow microwave system developed by the academic-industrial alliance between the University of Shizuoka, Advanced Industrial Science and Technology, and SAIDA FDS. In this Personal Account, we summarize the recent progress of our scalable microwave-assisted continuous synthesis using the SAIDA flow microwave apparatus.

Reconfigurable system for automated optimization of diverse chemical reactions

Chemical synthesis generally requires labor-intensive, sometimes tedious trial-and-error optimization of reaction conditions. Here, we describe a plug-and-play, continuous-flow chemical synthesis system that mitigates this challenge with an integrated combination of hardware, software, and analytics. The system software controls the user-selected reagents and unit operations (reactors and separators), processes reaction analytics (high-performance liquid chromatography, mass spectrometry, vibrational spectroscopy), and conducts automated optimizations. The capabilities of this system are demonstrated in high-yielding implementations of C-C and C-N cross-coupling, olefination, reductive amination, nucleophilic aromatic substitution (S_NAr), photoredox catalysis, and a multistep sequence. The graphical user interface enables users to initiate optimizations, monitor progress remotely, and analyze results. Subsequent users of an optimized procedure need only download an electronic file, comparable to a smartphone application, to implement the protocol on their own apparatus.

Optimum catalyst selection over continuous and discrete process variables with a single droplet microfluidic reaction platform

A mixed-integer nonlinear program (MINLP) algorithm to optimize catalyst turnover number (TON) and product yield by simultaneously modulating discrete variables—catalyst types—and continuous variables—temperature, residence time, and catalyst loading—was implemented and validated.

Ligand libraries for high throughput screening of homogeneous catalysts

This review describes different approaches to construct ligand libraries towards high throughput screening of homogeneous metal catalysts.

Optimizing Chemical Reactions with Deep Reinforcement Learning

Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability.

Automating drug discovery

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

Why microfluidics? Merits and trends in chemical synthesis

The intrinsic limitations of conventional batch synthesis have hindered its applications in both solving classical problems and exploiting new frontiers. Microfluidic technology offers a new platform for chemical synthesis toward either molecules or materials, which has promoted the progress of diverse fields such as organic chemistry, materials science, and biomedicine. In this review, we focus on the improved performance of microreactors in handling various situations, and outline the trend of microfluidic synthesis (microsynthesis, μSyn) from simple microreactors to integrated microsystems. Examples of synthesizing both chemical compounds and micro/nanomaterials show the flexible applications of this approach. We aim to provide strategic guidance for the rational design, fabrication, and integration of microdevices for synthetic use. We critically evaluate the existing challenges and future opportunities associated with this burgeoning field.

The Hitchhiker's Guide to Flow Chemistry ∥

Flow chemistry involves the use of channels or tubing to conduct a reaction in a continuous stream rather than in a flask. Flow equipment provides chemists with unique control over reaction parameters enhancing reactivity or in some cases enabling new reactions. This relatively young technology has received a remarkable amount of attention in the past decade with many reports on what can be done in flow. Until recently, however, the question, "Should we do this in flow?" has merely been an afterthought. This review introduces readers to the basic principles and fundamentals of flow chemistry and critically discusses recent flow chemistry accounts.

Synthesis of Phenylpropanoids via Matsuda-Heck Coupling of Arene Diazonium Salts

The Pd-catalyzed Heck-type coupling (Matsuda-Heck reaction) of electron rich arene diazonium salts with electron deficient olefins has been exploited for the synthesis of phenylpropanoid natural products. Examples described herein are the naturally occurring benzofurans methyl wutaifuranate, wutaifuranol, wutaifuranal, their 7-methoxy derivatives, and the O-prenylated natural products boropinols A and C.