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Machine learning for sustainable reaction design: A combined computational and experimental approach


   The Centre for Sustainable and Circular Technologies

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  Dr Matthew Grayson, Prof David Lupton  No more applications being accepted  Competition Funded PhD Project (European/UK Students Only)

Bath United Kingdom Artificial Intelligence Computational Chemistry Data Analysis Machine Learning Organic Chemistry Synthetic Chemistry

About the Project

The Centre for Sustainable & Circular Technologies (CSCT) at the University of Bath is inviting applications for the following PhD project which is part of a joint PhD programme between the University of Bath and Monash University in Australia. 

This project is one of a number that are in competition for up to four funded studentships. 

Home institution: University of Bath

Supervisor(s) at Bath: Dr Matthew Grayson (lead)

Supervisor(s) at Monash: Prof David Lupton

The computational design of new reactions is regarded as one of the “Holy Grails” of computational organic chemistry and biochemistry.1 Accurate and fast computational approaches to predicting chemical reactivity will provide cost-effective alternatives to experimental trial-and-error, and in some cases animal testing methods, in drug design, toxicology and chemical synthesis. Of great importance are explainable, mechanism-based prediction models which are more likely to reach general acceptance compared to black-box approaches. Providing clear insight into how and why predictions are made is particularly important for accountable chemical risk assessment and drug design.2 This project will combine the speed of ML with descriptors derived from QM calculations in a synergistic ML-QM approach to explainable and rapid high-accuracy reactivity prediction.

This project will train ML models to predict reaction barriers derived from high level DFT; such DFT calculations are far too time-consuming for use in reaction modelling studies. Using our ML-QM approach, rapid and accurate predictions will be possible even on a laptop and will represent a paradigm shift in reaction modelling. The pharmaceutical industry has called for new “robust, effective methodology to model industrially relevant organic molecules and reactions”.3 Hence, this project will develop ML-QM models for Michael acceptor reactivity prediction for use in covalent drug design, toxicology and pharmaceutical drug synthesis planning. These models will be validated against experimental data from pharmaceutical companies. Experimental predictions will also be made for novel substrates and catalysts. These predictions will then be tested in the Lupton lab to further validate our models. Use of these models will lead to a reduction in experimental trial-and-error and thus a more sustainable approach to reaction design. Furthermore, these models could be used to optimise atom economy which aligns with the second principle of circular chemistry (maximise atom circulation).4

To apply:

We invite applications from Science and Engineering graduates who have, or expect to obtain, a first or upper second class degree and have a strong interest in Sustainable & Circular Technologies. 

You may express an interest in up to three projects in order of preference. See the CSCT website for more information.

Please submit your application to the Home institution of your preferred project. You should note, however, that you are applying for a joint PhD programme and applications will be processed as such.

If this is your preferred project, apply using the relevant Bath online application form.

Please quote ‘Bath Monash PhD studentship’ in the Finance section and the lead supervisor(s)’ name(s) and project title(s) in the ‘Your research interests’ section.  More information on applying to Bath may be found here.

If the Home institution of your preferred project is Monash, apply here.

Enquiries about the application process should be sent to [Email Address Removed].

Studentship eligibility

Funding for Bath-based projects, such as the one advertised here, is available to candidates who qualify for Home fee status only. In determining Home student status, we follow the UK government’s fee regulations and guidance from the UK Council for International Student Affairs (UKCISA). Further information may also be found within the university’s fee status guidance.

EU/EEA citizens who live outside the UK are unlikely to be eligible for Home fees and funding.

Funding for Monash-based projects is available to candidates of any nationality. 

Please see the CSCT website for a full list of available projects.


Funding Notes

Bath Monash PhD studentships include tuition fee sponsorship and a living allowance (stipend) for up to 42 months maximum. Note, however, that studentships for Bath-based projects will provide cover for Home tuition fees ONLY. See the ‘Studentship eligibility’ section above. Non-Australian nationals studying in Australia will be required to pay their own Overseas Student Health Cover (OSHC). Additional and suitably qualified applicants who can access a scholarship/studentship from other sources will be also considered.

References

References: [1] Acc. Chem. Res. 2017, 50, 539. [2] Toxicol. Sci. 2018, 165, 213. [3] Drug Discov. Today 2018, 23, 1203. [4] Nat. Chem. 2019, 11, 190.