Machine Learning Driven Reaction Screening in Continuous Flow

Rapid transition from drug discovery to manufacturing is critical for the supply of clinical trials and delivery of newly approved medicines. However, traditional workflows often lead to time-consuming and costly redesigns between the different stages of pharmaceutical development. Digitally coupling autonomous reaction screening and process optimisation platforms has the potential to significantly streamline this process through machine learning guided experimentation.

This project will investigate flow chemistry and machine learning approaches for the development of an automated reaction screening platform. New procedures for performing reaction screenings in continuous flow under process relevant conditions will be developed and integrated within an autonomous workflow. This platform will be digitally coupled with self-optimising platforms, where new methods for transfer learning between reaction screenings and process optimisations will be explored to accelerate the development of pharmaceutical processes.