Dr James Smith

Dr James Smith


After graduating from the University of Leicester, I initially embarked on a DPhil in structural biochemisry at the University of Oxford. I was soon lured over to the University of Cambridge by an offer of an industry-funded PhD in Pharmacology and Computer-aided Drug Design, with TeknoMed a multi-site consortium that later became DeNovo Pharmaceuticals Ltd. This opportunity in cutting-edge research into molecular recognition and inhibitor design of the kinome targets  opened a postdoctoral career in medicinal chemistry. Industry-related positions followed in computational chemistry (ligand-protein interactions and xenobiotic metabolism) funded by The Wellcome Trust,  AstraZeneca and Unilever. Starting at the Centre for Molecular Informatics, my  research into molecular recognition led to an invitation to join the former MRC Centre for Protein Engineering with Sir Alan Ferscht to design a class of peptidomimetics to inhibit critical tumourgenic protein-protein interactions centred around p53. Drug target discovery in the interactome has remained a theme in my research. 

Research Career prior to joining The University of Leeds
2004 – 2007,  University of Erlangen-Nuremburg, in Germany at the  Computer Chemistry Center and the Biophysics Group, Centre for Medical Physics and Technology.
2007 – 2010, Jacobs University Bremen as a Volkswagen Foundation-funded Research Fellow in Computational Systems Biology with Marc-Thorsten Hütt
2009 – 2016, Affiliated Lecturer in Network Biology and Computational Structural Biology at the University of Cambridge Cambridge Computational Biology Institute, Department of Applied Mathematics and Theoretical Physics  and under Sir Tom Blundell  at the Department of Biochemistry, part of the Genome3D and the Open Source Drug Discovery consortia.
2012 – 2013, Fixed-term Lectureship,  BBSRC-funded SysMIC DTP consortium, School of Biological Sciences, Birkbeck and the Research Department of Cell and Developmental Biology, University College London.   
2013 – 2016, MRC Senior Investigator Scientist in Computational Metabolomics & Mipidomics, at the former MRC Human Nutrition Research,  Elsie Widdowson Laboratory in Cambridge. 


  • School Taught Postgraduate Tutor & MNatSc Link Tutor
  • Course Module Coordinator

Research interests

James Smith is a University Lecturer in Biochemistry in Metabolic & Biophysical Modelling and joined the School in September 2016. His research group focusses  on  aspects of machanistic nutritional biochemistry and systems chemistry.  His nutrtitional interests lie in building statistical models useful for predicting emergent states in metabolism.  

External and International Collaborators

  • Nick Parker & Andrew Baggaley, School of Mathematics, Statstics and Physics, University of Newcastle, UK.
  • Chi-Ying Huang, Distinguished Professor of Institute of Biopharmaceutical Sciences, National Yang Ming University, Taiwan.
  • Garstka Malgorzata Anna, Xi'an Jiaotong University, Xi’an City, Shaanxi, China. - Diagnostic biomarker discovery in early gestational diabetes.
  • Ahmed M. Ibrahim, Mansoura, Dakahlia, Egypt - Adjusting gut microbe populations, potential  pro-phylactic nutritional strategies for metabolic syndrome.
  • Babiker Badri, Ahfad University for Women, Omdurman, Sudan -  Natural product-derived re-design of leishmanicidal agents.
  • Micheal J Wise, School of Physics, Mathematics & Computing, University of Western Australia, Australia.  
  • David Hauton, University of Oxford, UK.
  • Albert Koulman, Core Metabolomics and Lipidomics Laboratory, Metabolic Research Laboratories, University of Cambridge, UK. 
  • Valerie Speirs, The Institute of Medical Sciences, University of Aberdeen, UK.
  • Richard Foster, School of Chemistry, University of Leeds, UK - Manganese-metallo protease inhibitor drug design.

Local Recognition

  • Shortlisted Nominee for the Inspirational Teaching Award from the Faculty of Mathematics and Physical Sciences, University of Leeds Partnership Awards 2018.

Professional Activities

  • NIHR Peer reviewer
  • European Commission H2020 Registered Expert Evaluator
  • Peer Reviewer for Computational & Mathematical Methods in Medicine, Journal of Theoretical Biology

Recent Funding

Current Postgraduate Researchers

  • Ms Yaowei Xun & Ms Nienyun Sharon Hsu, co-supervised by Richard Bayliss, School of Biological Sciences. Drug target discovery and peptide-based drug design for novel Basal-subtype breast cancer chemotherapy targetting intrinsically disordered regions in memrane-associated signalling domains.
  • Mr John W. Holden, jointly funded by DEFRA (HMGov UK) and the University of Newcastle,  co-supervised by  Nick Parker & Andrew Baggaley, School of Mathematics, Statstics and Physics, University of Newcastle, with Melvin Holmes & Rammile Ettelaie, School of Food Science & Nutrition  - Lattice-based epidemic spread - Novel forecasting of disease to  inform effective containment strategies.
  • Ms Yuwei Li, co-supervised with Francisco M. Goycoolea, School of Food Science & Nutrition - Modelling bacterial colony spatio-temporal patterns on microfluidic droplet surfaces - Rock-paper-scissor dynamics, reciprical altruism and dispersity of microbial populations. 
  • Ms Riannnon Morris,  co-supervised with Helen ChappellAntonia Borrisova  & Andrew Scott (SCAPE)
  • With Deirdre Devine (Sch of Dentristry) & Andrew Scott (SCAPE) co-supervising  Mr Oliver Hills  supervised by Helen Chappell.
<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://environment.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>


  • PhD University of Cambridge
  • BSc (Hons) University of Leicester

Professional memberships

  • Biochemistry Society
  • British Biophysical Society
  • Mathematical Biology Society

Student education

  • PGT Tutor
  • 2016-2020 Module Lead & Coordinator for both FOOD 2031 & FOOD 5241M  Nutritional Chemistry & Food Biochemistry
  • 2016-2019 Network and Systems Modelling for FOOD 3130 Food Research: Recent Revelations and Disputes,

Undergraduate and Postgraduate Research Projects

I am  looking for motivated research students interested in being trained  in any of the following multi-disiplinary areas: medicinal & biological chemistry, structural biochemistry & membrane pharmacology, metabolic & nutritional state prediction (eg in health and disease), metabolic modelling, computational systems biology, mathematical biology, biomedical statistics and machine learning.

Every year, I  host Summer Vacation internships as additional project students and have collaborators interested in sharing students. Enquire by email early in the New Year, if you are interested in joining my research group for a scholarship-funded project during the Summer - A number of organisations and societies,  listed here and here offer funding and scholarships. 

Former and Current Research Students

  • M Harland (2016-7) with David Hauton - Regressing QT and RR interval signals to both logarithmic and parabolic to determine  the accuracy of QTc interval changes during cardiovascular exercise under nitrate supplementation.
  • A  Bawden (2016-7) 2D machine learning stratification of both fat lipid biomarkers and participants useful for molecular epidemiology and fish-oil intervention studies.
  • A  Ackroyd (2016-7) with Alan Mackie - Time-dependent correlation clustering analysis to reveal coherence between subjective self-reported satiety, with objective data collected from lateral MRI imaging and circulating blood biomarkers both linked to gastric emptying.
  • W Pan (2017) Explicit solvation - Hydrogen-bonding networks determined from conformational sampling of MD-generated conformation ensembles (under AMBER) followed by geometry optimisation with DFT (under CASTEP). 
  • M  Liu (2017) Discrimination of Her2 and Luminal A breast cancer patients revealed by hierarchical clustering of nuclear receptor expression patterns.
  • H Tong (2017) University of Leeds and EPSRC-funded Summer Studentship: Canonical correlation analysis  and combinatoric composition analysis of acyl abundance in phospholipids and neutral acylglycerol, derived from large scale lipidomics. 
  • D Wu (2017) Identification of potential drug targets from hub-associated motifs from nuclear receptor-based expression correlation networks, found in distinct subgroups of basal (triple-negative) breast cancer patients. 
  • Z Ma (2017) Molecular recognition of isosterol and triterpenoid-based ligand-binding constraints to inform structure-based drug design.
  • W Zhou (2018) Characterising PPI binding motifs in intrinsically disordered regions used by KIT protein signalling, for peptidomimetic drug target discovery in signalling protein aggregates in breast cancer. 
  • Y Li (2018) Identification of functional motifs from GAB1 intrinsically disordered regions and an assessment of their stabilising interactions with SHC binding domains.
  • L Yeo (2018)  Phase I hydroxylation metabolic prediction of xenobiotics - A comparison of  mechanistic biotransformations by CYP2D6 and CYP2C9  predicted using SPORCalc.
  • K Daly (2018) Erasmus+ Project Student from DIT Dublin, Ireland: Quantitative methods for exploring metabolomics:
    i) Biostatistical methods for anthropometry landscape modelling according to dominant phospholipids in circulating lymphocytes. 
    ii) Unsupervised machine learning pattern recognition to characterise physiological biomarkers indicative of gestational diabetes in the first trimester. 
  • L Potts (2018-9)  Identifying unusual metabolic biomarker correlations in clinical data from early gestational diabetes.
  • S Holmes (2018-9) Rock-paper-scissor dynamics in bacteria - Development of a cellular automaton model for interacting gut bacterial colonies.
  • G Gornall (2018-9) Water activity and hydration shells of polyphenols with Helen Chappell.
  • H Tong (2018-9) Modelling interactions of aflatoxins and their metabolites with calcium phosphate, co-supervised with Helen Chappell.
  • Y Zhang (2019) Systematic review  of the thyroid and anaemia biomarkers on early gestational diabetes.
  • T He (2019) Systematic review of the hepatic and trace element biomarkers on early gestational diabetes.
  • A Shen (2019) Systematic review of coagulation biomarkers on early gestational diabetes. 
  • J Shu (2019) Microbial and metabolic modelling of Stag Hunt dyadic competition in Rock-Paper-Scissor interactions, co-supervised with Yuwei Li.
  • Yaowei Xun (2019) Molecular docking of SHC domains to short linear motifs from  intrinsically disordered regions  realted to intracellular signalling, co-supervised with Nienyun Sharon Hsu.
  • O Taylor (2019) Wellcome Trust-funded Summer Studentship: An unsupervised multivariate analysis of early gestational diabetes clinical biomarkers.
  • Y Jiang (2019-20) Computational Systems Biochemistry – Redundant Cyclic and Feed Forward motif behaviour from flux patterns of analplerotic metabolism.
  • E Wong (2019-20) Computer-aided Drug Design of Peptide inhibitor binding  for Triple Negative Basal Breast Cancer.
  • O Taylor (2019-20) Critical examination of neuraceutical composition in enteric feeds for metabolic nudging  in patients with acute sarcopenia.
  • T Liu & CW Chan (2019-20) Statistical modelling and clustering of blood preassure measurements.


Research groups and institutes

  • Nutrition and Public Health
  • Obesity, Cancer and Metabolic Disease

Current postgraduate researchers

<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://phd.leeds.ac.uk">research opportunities</a> allow you to search for projects and scholarships.</p>
    <li><a href="//phd.leeds.ac.uk/project/148-bayesian-clustering,-dirichlet-process-mixture-modelling-and-metabolic-profile-landscape-analysis-of-fat-and-lipid-biomarkers-derived-from-large-scale">Bayesian clustering, Dirichlet process mixture modelling and metabolic profile landscape analysis of fat and lipid biomarkers derived from large-scale</a></li>