Using Machine Learning to Access Challenging Hydrogenations: A combined theoretical and experimental approach

This project will use machine learning methodologies to probe the conditions necessary to undertake efficient asymmetric hydrogenation reactions on tetra-substituted carbon- carbon double bonds to form 1,2- contiguous stereocentres. Formation of such novel stereocentres allows for movement into three-dimensional space for access to new molecules and pharmaceuticals.

Asymmetric hydrogenations of tetra-substituted carbon-carbon double bonds are challenging particularly in obtaining high enantiomeric-excess values. To solve this problem machine learning techniques will be employed for the prediction of such values.

This will be achieved through the development of a dataset from available literature examples of asymmetric hydrogenation reactions. This dataset will be used for supervised machine learning methodologies for the development of predictive models. Reaction screening will also be undertaken in the lab to enrich the dataset and feed in to the machine learning models using a 'feedback loop' approach.

This project is half computational and half experimental as such this project is joint supervised with Dr Ruth Webster for experimental research and Dr Matthew Grayson for computational.