From the start of a synthetic chemist’s training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher’s career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

Visible-light photoredox reactions have been demonstrated to be powerful synthetic tools to access pharmaceutically relevant compounds. However, many photoredox reactions involve insoluble starting materials or products that complicate the use of continuous flow methods. By integrating a new solid-feeding strategy and a continuous stirred-tank reactor (CSTR) cascade, we realize a new solid-handling platform for conducting heterogeneous photoredox reactions in flow. Residence time distributions for single phase and solid particles characterize the hydrodynamics of the heterogeneous flow in the CSTR cascade. Silyl radical-mediated metallaphotoredox cross-electrophile coupling reactions with an inorganic base as the insoluble starting material demonstrate the use of the platform. Gram-scale synthesis is achieved in 13 h of stable operation.

Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices.

The first demonstration of aqueous surfactant-enabled Suzuki–Miyaura couplings run under flow conditions is described.

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Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices. Closed-loop optimization featuring active Machine Learning (ML) represents a powerful strategy for automating reaction optimization. However, the translation of chemical reaction conditions into a machine-readable format comes with the challenge of finding highly informative features which accurately capture the factors for reaction success and allow the model to learn efficiently. Herein, we compare the efficacy of different calculated chemical descriptors for a high throughput generated dataset to determine the impact on a supervised ML model when predicting reaction yield. Then, the effect of featurization and size of the initial dataset within a closed-loop reaction optimization was examined. Finally, the balance between descriptor complexity and dataset size was considered. Ultimately, tailored descriptors did not outperform simple generic representations, however, a larger initial dataset accelerated reaction optimization.

Organic reaction optimization for batch to flow transfer represents a main challenge for process chemists in drug synthesis. Several factors such as reactant concentration, residence/reaction time, or homo-/heterogeneity need to be taken into consideration during the fine-tuning of reaction conditions toward typical scale-up goals, such as high space–time yield. Herein, we present reaction optimization for photoredox iridium–nickel dual catalyzed cross-electrophile coupling with a focus on developing homogeneous starting conditions. During the screening, special attention was put on the replacement of inorganic bases with homogeneous organic bases, and the effect of pK a on the reaction yield was investigated. Screening was conducted via an automated segmented flow reactor at 15 μL scale, and subsequentially, the conditions were transferred to a 5 mL photo-continuous stirred-tank reactor (CSTR) cascade to demonstrate multigram continuous flow synthesis during a 24 h steady operation.

Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices. Closed-loop optimization featuring active Machine Learning (ML) represents a powerful strategy for automating reaction optimization. However, the translation of chemical reaction conditions into a machine-readable format comes with the challenge of finding highly informative features which accurately capture the factors for reaction success and allow the model to learn efficiently. Herein, we compare the efficacy of different calculated chemical descriptors for a high throughput generated dataset to determine the impact on a supervised ML model when predicting reaction yield. Then, the effect of featurization and size of the initial dataset within a closed-loop reaction optimization was examined. Finally, the balance between descriptor complexity and dataset size was considered. Ultimately, tailored descriptors did not outperform simple generic representations, however, a larger initial dataset accelerated reaction optimization.