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Dr Nick Furnham

PhD

Associate Professor
in Structural and Computational Biology of Infectious Disease

Room
361

LSHTM
Keppel Street
London
WC1E 7HT
United Kingdom

Tel.
8374

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

Department of Infection Biology
Faculty of Infectious and Tropical Diseases

Centres

Antimicrobial Resistance Centre

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.

Research Area
Bacteria
Drug discovery and development
Drug resistance
Helminths
Viruses
Chemotherapy
Medicines
Methodology
Protozoa
Modelling
Discipline
Bacteriology
Biochemistry
Genetics
Molecular biology
Pharmacology
Bioinformatics
Disease and Health Conditions
Infectious disease
Tuberculosis
Allergy
Hospital acquired infection
Neglected Tropical Diseases (NTDs)
Schistosomiasis
Tropical diseases
Country
Brazil
United Kingdom
Netherlands
Region
World

Selected Publications

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.
Moreira-Filho JT; Silva AC; Dantas RF; Gomes BF; Souza Neto LR; Brandao-Neto J; Owens RJ; Furnham N; Neves BJ; Silva-Junior FP
2021
Frontiers in immunology
Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
Portelli S; Myung Y; Furnham N; Vedithi SC; Pires DEV; Ascher DB
2020
Scientific reports
Combining structure and genomics to understand antimicrobial resistance.
Tunstall T; Portelli S; Phelan J; Clark TG; Ascher DB; Furnham N
2020
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis.
Coll F; Phelan J; Hill-Cawthorne GA; Nair MB; Mallard K; Ali S; Abdallah AM; Alghamdi S; Alsomali M; Ahmed AO
2018
Nature genetics
Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents
Giuliani S; Silva AC; Borba JVVB; Ramos PIP; Paveley RA; Muratov EN; Andrade CH; Furnham N
2018
Understanding molecular consequences of putative drug resistant mutations in Mycobacterium tuberculosis.
Portelli S; Phelan JE; Ascher DB; Clark TG; Furnham N
2018
Scientific reports
Comparisons of Allergenic and Metazoan Parasite Proteins: Allergy the Price of Immunity.
Tyagi N; Farnell EJ; Fitzsimmons CM; Ryan S; Tukahebwa E; Maizels RM; Dunne DW; Thornton JM; Furnham N
2015
PLoS computational biology
Exploring the evolution of novel enzyme functions within structurally defined protein superfamilies.
Furnham N; Sillitoe I; Holliday GL; Cuff AL; Laskowski RA; Orengo CA; Thornton JM
2012
PLoS computational biology
Missing in action: enzyme functional annotations in biological databases.
Furnham N; Garavelli JS; Apweiler R; Thornton JM
2009
Nature chemical biology
Assembly and channel opening in a bacterial drug efflux machine.
Bavro VN; Pietras Z; Furnham N; Pérez-Cano L; Fernández-Recio J; Pei XY; Misra R; Luisi B
2008
Molecular cell
Structure and mechanism of drug efflux machinery in Gram negative bacteria.
Pietras Z; Bavro VN; Furnham N; Pellegrini-Calace M; Milner-White EJ; Luisi BF
2008
Current drug targets
Is one solution good enough?
Furnham N; Blundell TL; DePristo MA; Terwilliger TC
2006
Nature structural & molecular biology
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