Theme 1: Computational & Data-Driven Structural Approaches to Drug Discovery
Software underpins all aspects of the drug discovery pipeline, including cheminformatics, immunoinformatics, computational chemistry, biomolecular simulation, robotics, and structural bioinformatics. To exploit fully the almost exponential growth in the size of databases for small molecules, biomolecular structures, bioactivities, and sequences, we need to develop new insights, algorithms and models for better drug discovery using techniques, from machine learning and AI to QSAR.
Theme 2: Cellular Microscopy and Image Analysis Underpinning Biomedical Discovery
Biomedical imaging technology has provided the tools needed to study biomedical processes directly within living systems. Imaging methods span multiple scales, from single molecule through to whole organisms. Common to all techniques is an increasing reliance on advanced methods for image analysis and artificial intelligence-aided interpretation.
Theme 3: Physiological Modelling Underpinning Biomedical Discovery
A key challenge in the development of novel prevention, prognostics, diagnostics, drugs, and therapies is obtaining a detailed understanding of how diseases evolve, and subsequently how treatment interventions interact and affect the complex physiological processes that constitute living organisms. This requires the development of biophysically-consistent mathematical models that can be integrated with multiple types of functional data into a consistent quantitative and predictive theoretical framework. The resulting models typically describe multiple physical processes, often occurring across a range of spatial and temporal scales, yielding solutions only via computational approaches.
You can find out more about the Supervisors in each of these Research Themes.