Digitalising the Development of Chemoenzymatic Cascades

This project aims to create a new artificially intelligent continuous flow platform for the development of multistep chemical and biocatalysed reactions. Pharmaceuticals are complex molecules which require multiple transformations to synthesise from readily available starting materials. Traditionally they are produced via batch manufacturing, where after each step intermediates are stored in containers or shipped to other facilities around the world to complete the manufacturing process. This adds a significant amount of processing time, contributes to a large carbon footprint, and is at significant risk of supply chain disruptions. In contrast, continuous manufacturing addresses each of these challenges by enabling end-to-end production within the same facility.

Catalysts are substances which are added to reactions which influence the rate and/or outcome of the reaction without been consumed. A well-designed catalyst will minimise the generation of waste by being highly selective, recyclable, and only required in very small quantities, often replacing the use of larger amounts of toxic reagents. Hence, it is economically and environmentally desirable to include multiple catalysed steps in a manufacturing process. Alone, the benefits of catalysis and continuous flow are becoming increasingly relevant due to the drive for decarbonisation, but in combination, they have the potential to truly transform the next generation of sustainable manufacturing. However, combining different types of catalysis into continuous flow processes remains highly challenging, due to poor compatibility between catalysts and the large number of variables that need to be optimised.

In this project we will develop a fully autonomous and artificially intelligent multistep continuous flow platform, which is capable of simultaneously optimising interconnected catalytic reactions. New multipoint analysis and automated reconfiguration capabilities will enable the creation of individual feedback loops for each reaction, which will be driven by machine learning algorithms suitable for multiobjective and mixed variable systems. We will then demonstrate this approach for the optimisation of industrially relevant chemoenzymatic cascades in sustainable and mutually compatible reaction media (e.g., deep eutectic solvents), thus combining the versatile reactivity of chemocatalysis with the high selectivity of biocatalysis.