Big Data and Machine Learning for Reaction Design


   Centre for Accountable, Responsible and Transparent AI

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  Dr Matthew Grayson  No more applications being accepted  Self-Funded PhD Students Only

About the Project

We have an exciting opportunity to work on the development of new synthesis prediction tools. You will join the Grayson group in the Department of Chemistry to develop fast and accurate synthesis prediction models using machine learning (ML) for applications in organic and pharmaceutical chemistry. To demonstrate their broad applicability, you will then work with our experimental collaborators to apply the models in reaction design.

As a PhD student, you will:

·        Gain experience in the use of ML in reaction design.

·        Perform both individual and collaborative research projects.

·        Write up research results for publication in scientific journals.

·        Present your work at national and international conferences.

Project Details:

The synthesis of new molecules is essential in meeting the global demand for new pharmaceutical drugs. Reaction discovery is dominated by trial-and-error approaches but more cost-effective, rapid and sustainable alternatives are becoming increasingly sought after.

Although computational approaches (e.g. quantum mechanical calculations) have been applied to reaction design, they are much slower than traditional experimental screening methods which limits their use in reaction discovery. Therefore, computational reaction design is still regarded as one of the “Holy Grails” of computational chemistry.

ML offers new opportunities for reaction design. ML models can, once trained, make predictions for previously unseen molecules in seconds compared to the weeks or months it takes to make such predictions using current computational approaches.

This project will develop ML models that can rapidly predict reaction outcomes (e.g. yield, enantioselectivity). To validate these models, predictions made for novel reactions will then be tested in collaboration with our experimental partners. Use of these models will help eliminate experimental trial-and-error and provide a more rapid approach to reaction discovery thus helping to realise the “Holy Grail”.

We are happy to adapt the project to better match the interests of the student. Please contact Dr Grayson to discuss this further. Further information about the Grayson group and our research interests can be found at thegraysongroup.co.uk and in references [1] and [2].

Formal applications should be made via the University of Bath’s online application form. Further information about the application process can be found here.

Start date: Between 8 January and 30 September 2024.


Chemistry (6) Computer Science (8) Mathematics (25)

Funding Notes

We welcome applications from candidates who can source their own funding. Tuition fees for the 2023/4 academic year are £4,700 (full-time) for Home students and £26,600 (full-time) for International students. For information about eligibility for Home fee status: https://www.bath.ac.uk/guides/understanding-your-tuition-fee-status/.

References

[1] Machine learning activation energies of chemical reactions. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2022, 12, e1593. https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1593
[2] Machine learning and semi-empirical calculations: A synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction. Chem. Sci., 2022, 13, 7594. https://pubs.rsc.org/en/content/articlelanding/2022/SC/D2SC02925A

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