Advancing synthesis prediction with machine learning - A data driven/mechanistic approach

The project will apply the latest machine learning (ML) techniques to chemical applications, including the exploration of reaction pathways toward medicinally relevant scaffolds. The aim will be to develop interpretable ML algorithms that facilitate the prediction of synthetic routes and provide a mechanistic understanding of their outcome.

This project will enable the student to explore fundamental scientific questions at the interface of chemistry and machine learning and apply these insights to tackle timely real-world applications. It will also provide the opportunity to work with multi-disciplinary teams in academia and industry. The group of Prof. Fernanda Duarte will provide world-leading expertise in reaction pathway modelling and automation, while the team at IBM Research will bring expertise in the development of computational chemistry software and AI techniques.