PhD Project in Learning to engineer drugs

When we are ill we will often take a drug that we hope will make us better, but many drugs that are very effective at making us better also have side-effects that can be much less desirable. One reason why this may happen is that the drug activates (or deactivates) several different biological signalling pathways simultaneously, and there is evidence for this in a particular class of molecules called G protein coupled receptors (GPCRs) which are often involved in a large number of signalling pathways. Elimination of side effects requires us to adjust the chemical structure of the drug to trigger signalling pathways much more selectively. This requires us to understand the molecular basis of drug action in much more detail. Recent work in the supervisor’s lab has used machine learning techniques on a large set of molecular docking poses to identify key interactions between a drug and its target that are associated with the triggering of specific biological pathways, and to construct a uniquely detailed structure-activity relationship that identifies how small changes to a drug can radically change their pharmacological activity and thus help to guide the drug discovery process. The next step is to extend this work to look at much larger numbers of drug targets using large publicly available datasets to refine and extend our technique and generalise our early findings.

In this project we will develop novel machine-learning approaches to identify the fine-grained interactions that control the effects of drugs. We will use a dataset derived from a combination of publicly available data and computational docking poses to learn the complex relationships between the many interdependent variables and incorporate our existing knowledge of molecular structure and function. We will develop new methods to learn specific features of a protein residue that can be used to predict the receptors contribution to signalling. We will also incorporate molecular structural information to generalise predictions to other drug targets. The project will involve a range of machine learning techniques such as graph neural networks which have been shown to be effective at representing molecular structures, and potentially novel applications of language models that have been shown to be effective in predicting molecular properties.

Funding Information

To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.

To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:

• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.

Candidates from ROI may also qualify for Home student funding.

Previous PhD study MAY make you ineligible to be considered for funding.

Please note that other terms and conditions also apply.

Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.

A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.

Academic Requirements:

The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.

Entrance requirements

Graduate
The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.

International Students

For information on international qualification equivalents, please check the specific information for your country.

English Language Requirements

Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component or equivalent qualification acceptable to the University is required (*taken within the last 2 years).

International students wishing to apply to Queen’s University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.