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Machine Learning and Reaction Modelling: A Synergistic Approach to Rapid Reactivity Prediction

   Department of Chemistry

Bath United Kingdom Computational Chemistry Data Analysis Data Science Machine Learning Organic Chemistry Pharmaceutical Chemistry Synthetic Chemistry

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, accurate and mechanism-based reactivity prediction models by combining machine learning (ML) with reaction modelling for applications in organic synthesis, covalent drug design and toxicology. To demonstrate their broad applicability, you will then apply the models to rationalising experimental reactivity data of organic and biological reactions for which the use of traditional modelling approaches would be prohibitively slow.

As a PhD student, you will:

  • Gain experience in the use of ML and reaction modelling in organic synthesis, covalent drug design and toxicology.
  • Perform both individual and collaborative research projects.
  • Write up research results for publication in scientific journals.
  • Disseminate your work through presentations at national and international conferences.

Project Details:

The synthesis of new molecules is essential in meeting the global demand for new pharmaceutical drugs. Reaction and drug 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 reactivity design, they are much slower than traditional experimental screening methods which limits their use in reaction and drug discovery. Therefore, computational reactivity design is still regarded as one of the “Holy Grails” of computational chemistry.

ML offers new opportunities for reactivity 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 chemical reactivity. Use of these models will help eliminate experimental trial-and-error and provide a more rapid approach to reaction and drug discovery thus helping to realise the “Holy Grail”. These models will also help eliminate animal testing in toxicology.

Candidate Requirements:

We are looking for a highly motivated individual to join our group.  You should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent). A master’s level qualification would also be advantageous. Experience with coding (any language) is desirable but not essential. Experience with machine learning and reaction modelling is not essential.

Non-UK applicants must meet our English language entry requirement.

Enquiries and Applications:

We welcome informal enquiries about the position. Please get in touch with Dr Matthew Grayson () if you have any questions. Further information about the Grayson group and our research interests can be found on our website:

Formal applications should be made via the University of Bath’s online application form for a PhD in Chemistry (full time).

More information about applying for a PhD at Bath may be found on our website

We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.

If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.

Funding Notes

It is anticipated that studentship funding will be obtained for this project covering tuition fees, a stipend at the UKRI standard doctoral rate (£15,609 p/a in 2021/22) and a training support allowance of £1,000 p/a for up to 3.5 years. This advertisement will be updated when funding in confirmed. In the meantime, interested candidates are advised to contact the lead supervisor for further information.

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