The use of flow photochemistry and its apparent superiority over batch has been reported by a number of groups in recent years. To rigorously determine whether flow does indeed have an advantage over batch, a broad range of synthetic photochemical transformations were optimized in both reactor modes and their yields and productivities compared. Surprisingly, yields were essentially identical in all comparative cases. Even more revealing was the observation that the productivity of flow reactors varied very little to that of their batch counterparts when the key reaction parameters were matched. Those with a single layer of fluorinated ethylene propylene (FEP) had an average productivity 20% lower than that of batch, whereas three-layer reactors were 20% more productive. Finally, the utility of flow chemistry was demonstrated in the scale-up of the ring-opening reaction of a potentially explosive [1.1.1] propellane with butane-2,3-dione.

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.

Photoredox decarboxylative cross-coupling via iridium−nickel dual catalysis has emerged as a valuable method for C(sp 2 )−C(sp 3 ) bond formation. Herein we describe the application of a segmented flow ("microslug") reactor equipped with a newly designed photochemistry module for material-efficient reaction screening and optimization. Through the deployment of a self-optimizing algorithm, optimal flow conditions for the model reaction were rapidly developed, simultaneously accounting for the effects of continuous variables (temperature and time) and discrete variables (base and catalyst). Temperature was found to be a critical parameter with regard to reaction rates and hence productivity in subsequent scale-up in flow. The optimized conditions identified at microscale were found to directly transfer to a Vapourtec UV-150 continuous flow photoreactor, enabling predictable scaleup operation at a scale of hundreds of milligrams per hour. This optimization approach was then expanded to other halide coupling partners that were low-yielding in batch reactions, highlighting the practical application of this optimization platform in the development of conditions for photochemical synthesis in continuous flow.

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.

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