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Dr. Furnham’s group has a dynamic research program using an interdisciplinary approach combining structural biology and chemistry with computer science (machine learning and AI). Our research can be divided into gaining a fundamental understanding of molecular functions and the translation of the findings to important questions in infectious disease biology. The research group is currently focused on understanding molecular and functional evolution in the context of antimicrobial resistance working on a range of pathogens. A core of this work is in developing machine learning / AI driven computational tools to predict drug resistance underpinned by experimental validation and insight. By modelling how pathogens mutate to avoid the effect of drugs, we can better predict how infections will respond to specific drugs and can design drugs that have longer clinical use. It also benefits those managing prescribing routines and in surveillance, identifying new emerging resistance that can be acted on before it becomes widespread within a population.

The flip side to drug resistance is drug discovery and the group has an active research program in this area. We have developed new target identification methods, undertaken experimental structure-based high-throughput fragment screening campaigns, performed machine learning / AI driven hit identification as well as virtual and experimental (in vitro and ex vivo) compound screening and biophysical assays. The project was short listed for a prestigious Newton Award.