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Reactor automation is revolutionising the way new chemical processes are discovered and developed. Assigning repetitive aspects of chemical synthesis to machines, such as experimental execution and data collection, provides more time for researchers to focus on critical interpretation and creative problem solving. The ability to autonomously prepare late‐stage intermediates and complex products, rather than just simple starting materials, will play a central role in applications such as the efficient exploration of chemical space and responsive manufacturing. However, translating automated technologies from specific single‐step tasks to more general multistep syntheses remains a significant challenge, owing to high structural diversity and chemical/physical interdependencies between the steps. Robotic batch and continuous flow platforms are gradually becoming more universal, providing access to a wider range of chemistries required to achieve autonomous multistep synthesis. Advances in process analytical technologies have enhanced our ability to monitor interconnected reactions in real‐time, thus accelerating data collection and giving greater process control for ensuring a high standard of safety and product quality. Integration of these tools with control software creates a feedback loop, which can be harnessed for adaptive and flexible multistep screening or holistic self‐optimisation. This review presents recent developments in the application of automated reactor technologies for multistep chemical synthesis, including batch and continuous flow platforms. Specifically, this review highlights how the integration of control software with advanced process analytical technologies and machine learning algorithms are accelerating the synthesis of complex molecules. [ABSTRACT FROM AUTHOR]

The optimization of multistep chemical syntheses is critical for the rapid development of new pharmaceuticals. However, concatenating individually optimized reactions can lead to inefficient multistep syntheses, owing to chemical interdependencies between the steps. Herein, we develop an automated continuous flow platform for the simultaneous optimization of telescoped reactions. Our approach is applied to a Heck cyclization‐deprotection reaction sequence, used in the synthesis of a precursor for 1‐methyltetrahydroisoquinoline C5 functionalization. A simple method for multipoint sampling with a single online HPLC instrument was designed, enabling accurate quantification of each reaction, and an in‐depth understanding of the reaction pathways. Notably, integration of Bayesian optimization techniques identified an 81 % overall yield in just 14 h, and revealed a favorable competing pathway for formation of the desired product. [ABSTRACT FROM AUTHOR]

The optimization of multistep chemical syntheses is critical for the rapid development of new pharmaceuticals. However, concatenating individually optimized reactions can lead to inefficient multistep syntheses, owing to chemical interdependencies between the steps. Herein, we develop an automated continuous flow platform for the simultaneous optimization of telescoped reactions. Our approach is applied to a Heck cyclization‐deprotection reaction sequence, used in the synthesis of a precursor for 1‐methyltetrahydroisoquinoline C5 functionalization. A simple method for multipoint sampling with a single online HPLC instrument was designed, enabling accurate quantification of each reaction, and an in‐depth understanding of the reaction pathways. Notably, integration of Bayesian optimization techniques identified an 81 % overall yield in just 14 h, and revealed a favorable competing pathway for formation of the desired product. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Multi-objective algorithm applied to the self-optimization of flow reactor. • Algorithm simultaneously targeted reactor productivity and environmental objectives. • Pareto front shows the trade-off between these target objectives. • Gaussian process models provide knowledge about the nature of interactions. Abstract Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance criteria. Herein we report the implementation of a new multi-objective machine learning optimization algorithm for self-optimization, and demonstrate it in two exemplar chemical reactions performed in continuous flow. The algorithm successfully identified a set of optimal conditions corresponding to the trade-off curve (Pareto front) between environmental and economic objectives in both cases. Thus, it reveals the complete underlying trade-off and is not limited to one compromise as is the case in many other studies. The machine learning algorithm proved to be extremely data efficient, identifying the optimal conditions for the objectives in a lower number of experiments compared to single-objective optimizations. The complete underlying trade-off between multiple objectives is identified without arbitrary weighting factors, but via true multi-objective optimization. [ABSTRACT FROM AUTHOR]

A new application of Pd-catalysed allylation is reported that enables the synthesis of a range of branched sp3-functionalised sulfonamides, a compound class for which few reported methods exist. By reacting benzyl sulfonamides with allylic acetates in the presence of Pd0 catalysts and base at room temperature, direct allylation was efficiently performed, yielding products that are analogues of structural motifs seen in biologically active small molecules. The reaction was performed under mild conditions and could be applied to nanomolar sigma-receptor binders, thus enabling a late-stage functionalisation and efficient expansion of drug-like chemical space. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mixed variable multi-objective algorithm applied to self-optimizing flow reactor. • Algorithm simultaneously optimized discrete and continuous reaction variables. • Key interactions between mixed variables provided greater process understanding. • Trade-off curves for selectivity, productivity and environmental impact identified. The consideration of discrete variables (e.g. catalyst, ligand, solvent) in experimental self-optimization approaches remains a significant challenge. Herein we report the application of a new mixed variable multi-objective optimization (MVMOO) algorithm for the self-optimization of chemical reactions. Coupling of the MVMOO algorithm with an automated continuous flow platform enabled identification of the trade-off curves for different performance criteria by optimizing the continuous and discrete variables concurrently. This approach utilizes a Bayesian methodology to provide high optimization efficiency, enhances process understanding by considering key interactions between the mixed variables, and requires no prior knowledge of the reaction. Nucleophilic aromatic substitution (S N Ar) and palladium catalyzed Sonogashira reactions were investigated, where the effect of solvent and ligand selection on the regioselectivity and process efficiency were determined respectively whilst simultaneously determining the optimum continuous parameters in each case. [ABSTRACT FROM AUTHOR]

• Multi-objective algorithm applied to the self-optimisation of complex pharmaceutical processes. • Algorithm targeted automated reactor productivity and downstream purification. • Pareto front rapidly generated for three competing objectives. There has been an increasing interest in the use of automated self-optimising continuous flow platforms for the development and manufacture in synthesis in recent years. Such processes include multiple reactive and work-up steps, which need to be efficiently optimised. Here, we report the combination of multi-objective optimisation based on machine learning methods (TSEMO algorithm) with self-optimising platforms for the optimisation of multi-step continuous reaction processes. This is demonstrated for a pharmaceutically relevant Sonogashira reaction. We demonstrate how optimum reaction conditions are re-evaluated with the changing downstream work-up specifications in the active learning process. Furthermore, a Claisen-Schmidt condensation reaction with subsequent liquid-liquid separation was optimised with respect to three-objectives. This approach provides the ability to simultaneously optimise multi-step processes with respect to multiple objectives, and thus has the potential to make substantial savings in time and resources. [ABSTRACT FROM AUTHOR]

Despite growing applications being reported both in academia and industry, continuous flow chemistry remains a relatively untaught field across most chemistry undergraduate courses. This is particularly true in laboratory practical classes, where it is often deemed simpler to carry out synthetic reactions in traditional batch mode using round-bottomed flasks. Herein, we report the development of an undergraduate project that utilises cheap and readily available materials to construct continuous flow reactors. The students compare the performance of different types of reactors and conditions in a biphasic selective acetylation of a symmetrical diamine. Throughout the investigation, the students can vary multiple parameters as they optimise the reaction, thus actively learning and readjusting them based on their improved understanding. The experiments give the students an appreciation of continuous flow techniques in comparison to batch. [ABSTRACT FROM AUTHOR]

Progress reaction profiles are affected by both catalyst activation and deactivation processes occurring alongside the main reaction. These processes complicate the kinetic analysis of reactions, often directing researchers toward incorrect conclusions. We report the application of two kinetic treatments, based on variable time normalization analysis, to reactions involving catalyst activation and deactivation processes. The first kinetic treatment allows the removal of induction periods or the effect of rate perturbations associated with catalyst deactivation from kinetic profiles when the quantity of active catalyst can be measured. The second treatment allows the estimation of the activation or deactivation profile of the catalyst when the order of the reactants for the main reaction is known. Both treatments facilitate kinetic analysis of reactions suffering catalyst activation or deactivation processes. [ABSTRACT FROM AUTHOR]

Progress reaction profiles are affected by both catalyst activation and deactivation processes occurring alongside the main reaction. These processes complicate the kinetic analysis of reactions, often directing researchers toward incorrect conclusions. We report the application of two kinetic treatments, based on variable time normalization analysis, to reactions involving catalyst activation and deactivation processes. The first kinetic treatment allows the removal of induction periods or the effect of rate perturbations associated with catalyst deactivation from kinetic profiles when the quantity of active catalyst can be measured. The second treatment allows the estimation of the activation or deactivation profile of the catalyst when the order of the reactants for the main reaction is known. Both treatments facilitate kinetic analysis of reactions suffering catalyst activation or deactivation processes. [ABSTRACT FROM AUTHOR]