-
: Machine Learning Molecular Dynamics. The project involves the development and application of machine learning methods that enable a major boost of the time and length scales accessible to ab-initio/first
-
for developing new treatments against drug-resistant infections. Their rapid action and ability to target bacteria in several ways make it difficult for antimicrobial resistance to emerge. Despite this promise
-
techniques. The findings will lay the groundwork for clinical application and contribute to the development of targeted therapies for resistant bacterial infections. Approach and Methods Atomic force
-
enzymes. Mapping bacterial defence systems to infer predictive features of co-evolutionary dynamics. Impact and Outlook This project will: • Advance understanding of microbial co-evolution. • Deliver a
-
challenging. This project aims to develop an ultrasound-assisted nanoparticle-based drug delivery system for targeted, controlled release of antimicrobials within these hard-to-reach oral microenvironments. By
-
to develop nanoengineered, slippery surface coatings that prevent bacterial adhesion and biofilm formation on orthopaedic implants without relying on antibiotics or toxic metals. By precisely tuning surface
-
. Synthetic analogues will be developed and screened alone and in combination with existing antimicrobials. The ultimate goal is to design novel chemotherapeutic combinations that disrupt cell wall remodelling
-
for engineering novel antimicrobial peptides. The findings could lead to the development of new therapeutic scaffolds with applications in infectious disease, biotechnology, and immunotherapy. The project also
-
promising targets for antiviral drug development. While the COVID-19 pandemic highlighted the threat of RNA viruses, large DNA viruses such as African Swine Fever Virus (ASFV) remain underexplored despite
-
diagnostics, empirical antibiotic use is common, exacerbating resistance. This project aims to develop a next-generation lateral flow assay (LFA) platform for rapid, ultrasensitive detection of RTI pathogens