The drug discovery process is highly complex requiring knowledge from a variety of domains including human diseases, genetics, structural biological and medicinal chemistry. These factors have prompted the search for reliable virtual (i.e. computer-based) screening methods to select likely drug candidates. While machine learning methods are now well established for the prediction of biological activities of small molecules, they have generally been applied to predict binding to a single biological target and do not consider more complex relationships such as the potential for side effects. Knowledge graphs are a recent construct that provide a way of organising data from multiple heterogeneous sources and have recently begun to be used in drug discovery. This project will develop graph theory techniques for extracting information-rich feature vectors from knowledge graphs for use with machine learning methods. The aim will be to provide more accurate predictions of the physicochemical and biochemical properties of small molecules.
The project is funded by EPSRC and Evotec and will involve a placement at Evotec with an expected duration of three months.
This PhD is available to UK students in possession of a degree at 2:1 or better. More details regarding the formal PGR requirements can be found at the following link. This project is suitable for chemistry, biology or computer science students with an interest in biomedical research, proven ability in maths and an interest in developing computer software.
UK applicants will be eligible for a full award paying fees and a stipend at the enhanced UKRI rate of £18.285 per annum. The funding also includes a generous research training grant.
Prospective applicants should send their CV and two reference letters to [Email Address Removed]. Pre-selected candidates will be invited to an interview.