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

The DPhil in Computational Discovery is a multidisciplinary programme spanning projects in Advanced Molecular Simulations, Machine Learning and Quantum Computing to develop new tools and methodologies for life sciences discovery.

This innovative course has been developed in close partnership between Oxford University and IBM Research. Each research project has been co-developed by Oxford academics working with IBM scientists. Students will have a named IBM supervisor/s and many opportunities for collaboration with IBM throughout the studentship.

The scientific focus of the programme is at the interface between Physical and Life Sciences. By bringing together advances in data and computing science with large complex sets of experimental data more realistic and predictive computational models can be developed. These new tools and methodologies for computational discovery can drive advances in our understanding of fundamental cellular biology and drug discovery. Projects will span the emerging fields of Advanced Molecular Simulations, Machine Learning and Quantum Computing addressing both fundamental questions in each of these fields as well as at their interfaces.

Students will benefit from the interdisciplinary nature of the course cohort as well as the close interactions with IBM Scientists.

Applicants who are offered places will be provided with a funding package that will include fees at the Home rate, a stipend at the standard Research Council rate (currently £17,668 pa) + £2,400 for four years. 

There are 16 projects available and you may identify up to three projects to be considered for in your application. The details of Project 16 are listed below.

There is no application fee to apply to this course. For information on how to apply and entry requirements, please see DPhil in Computational Discovery | University of Oxford.

Project 16

Title: Advancing synthesis prediction with machine learning – A data driven/mechanistic approach

PI: Fernanda Duarte

Summary: 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.

Applicants must have, or expect to obtain, a Master’s (or equivalent) degree in Chemistry, Physics, Computer science or a related subject. Previous experience in computational chemistry or machine learning would be an advantage.