Integrated Chemoproteomics and Machine Learning for Accelerated Anti-Klebsiella Drug Discovery

Stephen Dela Ahator of the University of Ghana in Ghana, will pioneer a project involving multidisciplinary platform combining chemoproteomics and machine learning to accelerate the discovery of next-generation antimicrobials against Klebsiella. Using activity-based protein profiling, the project aims to map the functional landscape of bacterial bioactive enzymes to identify evolutionarily conserved and druggable targets. A hybrid graph neural network model will then predict and prioritize small-molecule inhibitors with high specificity and low human cross-reactivity. Lead compounds will be experimentally validated for potency, selectivity, and safety in infection models. By integrating functional proteomics with AI-driven compound screening, this project will aim to deliver new therapeutic scaffolds, establish an adaptable antimicrobial discovery pipeline, and strengthen research capacity through international collaboration between Ghana, Norway, the UK, and New Zealand.

This grant is funded by The Wellcome Trust.