Primary supervisor - Prof Kate Kemsley
Secondary supervisor - Assoc Prof Katharina Huber
'Chemical space' is the vast, multidimensional landscape of all possible molecules that exist in nature or can be synthesized in the laboratory. Understanding the space is vital for natural product and drug discovery, but as yet only a tiny fraction of these so-called ‘small’ molecules has been fully characterized. This PhD project will use advanced computational techniques to explore this landscape. It is jointly funded by UEA and Mestrelab Research S.L., a global leader in software for the pharmaceutical, chemical and biotech industries.
If you have a strong background in computational chemistry, physics, mathematics or a related area, then this is the project for you! With access to large collections of labelled data, you will combine techniques from graph theory with recently developed AI approaches, such as graph convolutional neural networks, to develop predictive models that relate molecular structure to function. Although knowledge of chemistry or cheminformatics is desirable, it is not essential for you to successfully complete the project.
To get hands-on experience working with industry, the project will also provide you with the opportunity to spend time at Mestrelab’s company headquarters in Santiago de Compostela in Spain.
Informal enquiries concerning the project are welcomed by the primary supervisor [email protected] .
This 3 year PhD studentship is funded by Mestrelab Research S.L and the Faculty of Science. Applications are welcomed from UK applicants eligible for tuition fees at the ‘home’ rate only. Funding comprises ‘home’ tuition fees and an annual stipend of £18,622 (for a maximum of 36 months).
Entry requirements
The standard minimum entry requirement is 2:1 (Hons) in Physics, Chemistry, Mathematics or related subjects with strong mathematics content.
Mode of study
Full-time
Start date
October 2024
References