Dr Nick Furnham
PhD
Associate Professor
in Structural and Computational Biology of Infectious Disease
Nicholas has expertise in microbiology, computational biology, machine learning / AI, genomics and structural biology.
He joined the School as an independent investigator supported by a MRC Strategic Skill Fellowship in Methodology Research. Prior to this he was a staff scientist / post-doctoral research in the group of Prof. Dame Janet Thornton at the European Bioinformatics Institute (an outstation of the European Molecular Biology Laboratory). He completed his PhD under the supervision of Prof. Sir Tom Blundell in the Biochemistry Department at Cambridge University after a MSc. in Bioinformatics at Exeter University. His original undergraduate training in Biological Science at King’s College London.
Affiliations
Centres
Teaching
Nicholas runs the Novel Drug Discovery and AMR module that is open to several of the ITD faculty MSc programs. He also co-organises the Programming module (Python and R), one of the core modules of the Health Data Science MSc. run in the EPH faculty. Nicholas also contributes lectures and practical classes to several other MSc. modules including Pathogen Genomics, Molecular Biology and Recombinant DNA Techniques and Advanced Training in Molecular Biology. In addition, he supervises research students and those interested in undertaking a PhD or MSc project should contact him directly.
Research
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.
Selected Publications
