PhD Studentship: Machine Learning of Energy Barriers for Reaction Network Discovery of Drug-like Molecules

Supervisors: Prof. Reinhard Maurer, Prof. Scott Habershon

In drug discovery, millions of molecules need to be screened for their viability as drug candidate, including their synthetic viability. Yields of chemical reactions are often limited by the formation of unforeseen by-products, which are not accounted for in synthesis planning.

The exploration of kinetically accessible by-products requires the accurate prediction of reaction enthalpies and activation free energies for all relevant intermediates. In this project, a deep learning and generative design toolchain will be developed resulting in an ML model of reaction barriers.

This will enable the development of more accurate and advanced high-throughput reaction network discovery and by-product prediction.

Background

Typical drug molecules can contain up to 100 non-hydrogen atoms, which makes the development of cost-effective and efficient synthetic pathways very challenging. Effective retrosynthetic design requires the ability to predict accurate reaction enthalpies and activation free energies for relevant intermediates. While quantum chemical predictions typically can provide sufficient accuracy of prediction (~1kcal/mol error), they are not feasible at the scale of millions of predictions per day. The need to predict the transition state structure as input for quantum chemical barrier predictions adds further complications. Machine learning models of quantum chemistry can achieve fast and accurate predictions, but comprehensive data sets for reaction barriers of large molecules simply do not exist.

Several recent works have attempted to tackle the scarcity of data on reaction barriers by creating new curated data sets. However, these datasets only feature molecules up to 7 heavy atoms. Even though activation free energies and thermochemistry data might be available for small molecules, the complexity of large chemical reactions means that entropic contributions become even more relevant, particularly for bimolecular reactions. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of reaction enthalpies and activation free energies for realistic molecules. See references at hetsys@warwick.ac.uk

Project Aims

In this project, the student will develop a deep learning and generative design toolchain to accurately predict chemical reaction barriers without recourse to transition state structures and quantum chemical calculations at the point of prediction. This will enable the development of more accurate and advanced retrosynthetic design workflows. The project is in close collaboration with a leading pharmaceutical company and will involve an additional six-month industrial placement of the PhD student extending the overall project to 4.5 years. 

About HetSys

The EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys), based at the University of Warwick, is an exceptional environment for students from physical sciences, life sciences, mathematics, statistics, and engineering. HetSys specializes in applying advanced mathematical methods to tackle complex, real-world problems across a variety of research areas.

Our research themes span exciting topics such as nanoscale devices, innovative catalysts, superalloys, smart fluids, space plasmas, and more. HetSys provides:

• A vibrant, interdisciplinary student community.
• Flexible, tailored training opportunities.
• A collaborative environment that fosters creativity and growth.

Interested?

Join HetSys and help shape the future of sustainable technology through groundbreaking research. For more information about this project and how to apply, visit: https://warwick.ac.uk/fac/sci/hetsys/themes/projects2025.

Funding Details

Additional Funding Information

For more details visit: https://warwick.ac.uk/fac/sci/hetsys/apply/funding/

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