Dr Furnham is Associate Professor of Computational Molecular Biology at LSHTM and internationally recognised leader of a multidisciplinary AI-driven discovery group developing computational approaches for biologics engineering, antimicrobial resistance and drug discovery.
With over 15 years’ experience in computational biology, his work integrates machine learning, protein language models, chemi-informatics and structural bioinformatics to generate actionable insights from complex biological data and support rational therapeutic design.
He is founding Director of the Centre for Data and Statistical Science for Health at LSHTM, contributing to institutional strategy in artificial intelligence and data science. Dr Furnham also plays a national role in shaping UK research priorities through service on UKRI funding panels providing expertise on AI in health.
Affiliations
Centres
Teaching
Dr Furnham contributes extensively to postgraduate teaching and training at LSHTM, delivering education in computational biology, data science, and drug discovery to MSc students. He serves as Module Organiser for the core programming module (Python and R) within the Health Data Science MSc programme in the Faculty of Epidemiology and Population Health (EPH). He is also Module Organiser for Novel Drug Discovery and AMR module in the Faculty of Infectious and Tropical Diseases (ITD), a course open to students across MSc streams. Through these roles as well as contributing to other modules, he supports the development of computational and analytical skills for modern biomedical research. He supervises research degree students and those interested in undertaking a PhD or MSc/MRes project should contact him directly.
Research
Dr Furnham’s research centres on the development and application of advanced computational and AI methodologies to key challenges in infectious disease biology and therapeutic development. His work spans antimicrobial resistance prediction, novel drug target identification, small molecule development and biologics design, with a strong emphasis on translation into scalable discovery pipelines.
Key areas of research include the use of machine learning to predict resistance mutations and improve the durability of antimicrobial therapies; development of sequence- and structure-based models to identify allosteric drug targets; and the application of protein language models and generative AI to engineer high-affinity biologics such as nanobodies. His research also addresses viral evolution and immune escape, including work on norovirus and broader host–pathogen interactions.
Dr Furnham leads interdisciplinary programmes that integrate computational modelling with experimental and clinical data, including collaborative projects across academia and industry. His work has contributed to advances in fragment-based drug discovery, enzyme evolution, and computational structural biology, underpinning new strategies for tackling infectious diseases and antimicrobial resistance.
Selected Publications
