PhD Studentship: Deep Learning of Reaction Barriers for High-throughput Retrosynthetic Drug Design

Supervisors: Prof. Reinhard J. Maurer, Prof. Scott Habershon

Summary:

The drug discovery pipeline involves the screening of many molecules before viable leads are identified. This involves screening for their pharmacological properties, but also for their synthetic viability. Typical drug molecules can contain up to 100 non-hydrogen atoms, which makes the development of cost-effective and efficient synthetic pathways very challenging. Therefore, high-throughput screening of drug-like molecules needs to also consider their synthetic viability. The aim of this project is to develop a deep learning and generative design toolchain to accurately predict chemical reaction barriers that will advance chemical retrosynthetic design workflows.

In the exploratory phase of drug discovery, millions of molecules are screened for their viability as drugs. This involves screening for their pharmacological properties, but also for their synthetic viability. 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 (ML) 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 tried to tackle the scarcity of data on reaction barriers by creating new curated data sets, but data for large molecules remains scarce. Furthermore, entropic and solvent effects will play a crucial role in reactions of large drug molecules and need to be considered. Graph-based reaction discovery and generative machine learning 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.

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 synthesis planning.

The project is in collaboration with a leading pharmaceutical company and will involve an extended industrial placement.

About the CDT:

HetSys recruits enthusiastic students from across physical sciences, mathematics and engineering who enjoy using their mathematical skills and thinking flexibly to solve complex problems. By developing these skills HetSys trains people to challenge current state-of-the-art in computational modelling of heterogeneous, ‘real world’ systems across a range of research themes such as nanoscale devices, new catalysts, superalloys, smart fluids, space plasmas etc. They have recently been awarded £11m to train PhD cohorts in computation modelling.

Built around a closely knit, highly collaborative team of academics from five science departments at Warwick with a strong track record in leading large projects. With its project partners HetSys develops talented PhD students to push boundaries in this exciting field. The students have the potential to inspire new ideas, approaches and innovation and become future leaders in developing new technologies. HetSys builds on Warwick's cross-departmental scientific computing research community and the Warwick Centre for Predictive Modelling. 

https://warwick.ac.uk/fac/sci/hetsys/themes/projectopportunities/

Previous applicants need not apply.

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

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