Dr. Steve O’Hagan

Dr. Steve O’Hagan
Computer Officer
Manchester Institute of Biotechnology
University of Manchester
131, Princess St
Manchester M1 7DN
Dr Steve O'Hagan
Personal | Education | Publications | Miscellaneous | contact


Dr Steve O’Hagan

Place of birth: Glasgow, Scotland. Date of birth: 14th September 1959

ORCID iD iconorcid.org/0000-0001-6235-5462


Victoria University of Manchester BSc (Hons) Chemistry. Third year specialisation: Physical Chemistry, together with options in Mathematics and Computer Science. My third year project, with Prof. J.H. Baxendale, involved studying the interaction of heavy metal ions with micelles using conductometry, fluorescence quenching studies and pulse radiolysis. The work was carried out in the Paterson Institute for Cancer Research (formerly “The Paterson Laboratory”) at the Christie Hospital.

Victoria University of Manchester MSc Chemistry; thesis title ‘Molecular Beam Sources’. Used mass spectrometry and time-of-flight velocity measurements to characterise the molecular species present and energy distribution of novel molecular beam sources (for potential use in reactive scattering experiments). A significant part of the work involved computer simulation to predict reaction products within the beam source from chemical kinetic data.
My MSc supervisor was Prof. Roger Grice.

University of Warwick PhD Chemistry; thesis title ‘Analysis of Engine Oils & Additives by Mass Spectrometry’. A variety of mass spectrometry ionisation techniques were employed to analyse engine oils and their additives, the aim of which was to find methods of coping with the complex mixtures involved. The research included extensive use of chemometrics techniques to extract individual spectra from the mass spectra of mixtures.
PhD supervisor: Prof. K.R. Jennings


Samanta S, O’Hagan S, Swainston N, Roberts TJ, Kell DB (2020). VAE-Sim: a novel molecular similarity measure based on a variational autoencoder. bioRxiv 2020:172908. DOI: 10.1101/2020.06.26.172908.

Salcedo-Sora JE, Jindal S, O’Hagan S, Kell DB (2020). A palette of fluorophores that are differentially accumulated by wild-type and mutant strains of Escherichia coli: surrogate ligands for bacterial membrane transporters. bioRxiv 152629v1. DOI: 10.1101/2020.06.15.152629.

Wright Muelas M, Gueorguieva I, Mughal F, O’Hagan S, Day PJ, Kell DB (2020). An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines. bioRxiv 129239

Khemchandani Y, O’Hagan S, Samanta S, Swainston N, Roberts TJ, Bollegala D, Kell D B (2020). DeepGraphMol, a multiobjective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach. bioRxiv, 2020/114165.

O’Hagan S, Kell DB (2019). Structural similarities between some common fluorophores used in  biology and marketed drugs, endogenous metabolites, and natural products. bioRxiv:834325v1. DOI:10.1101/834325

Wright Muelas, M., Mughal, F., O’Hagan, S., Day, P. J. & Kell, D. B. (2019). The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data. Sci Rep 9, 17960. DOI: 10.1038/s41598-019-54288-7. Also bioRxiv, 18007.

O’Hagan S, Kell DB (2019)  Generation of a small library of natural products designed to cover chemical space inexpensively. Pharm Front 1:e190005. DOI:10.20900/pf20190005.

Kell DB, Wright Muelas M, O’Hagan S, Day PJ (2018). The role of drug transporters in phenotypic screening. Drug Target Review 4: 16-19.

O’Hagan S, Wright Muelas M, Day PJ, Lundberg E, Kell DB (2018) GeneGini: assessment via the Gini coefficient of reference ‘‘housekeeping’’ genes and diverse human transporter expression profiles. Cell Syst 6, 230-244. PMID 29428416. DOI https://10.1016/j.cels.2018.01.003

O’Hagan, S. & Kell, D.B. (2018). Analysing and navigating natural products space for generating small, diverse, but representative chemical libraries. Biotechnol. J., 1700503. doi:10.1002/biot.201700503.

Glymenaki M, Barnes A, O’ Hagan S, Warhurst G, McBain AJ, Wilson ID, Kell DB, Else KJ, Cruickshank SM (2017). Stability in metabolic phenotypes and inferred metagenome profiles before the onset of colitis-induced inflammation. Sci Rep 7, 8836. DOI:10.1038/s41598-017-08732-1

O’Hagan S, Wright Muelas M, Day PJ, Lundberg E, Kell DB (2017) Novel ‘housekeeping’ genes and an unusually heterogeneous distribution of transporter expression profiles in human tissues and cell lines, assessed using the Gini coefficient. bioRxiv 2017:155697. doi: https://doi.org/10.1101/155697

O’Hagan S, Kell DB (2017) Consensus rank orderings of molecular fingerprints illustrate the ‘most genuine’ similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates. ADMET & DMPK 5, 85-125. Pubmed. doi: 10.5599/admet.5.2.376

Grixti J, O’Hagan S, Day PJ, Kell DB (2017): Enhancing drug efficacy and therapeutic index through cheminformatics-based selection of small molecule binary weapons that improve transporter-mediated targeting: a cytotoxicity system based on gemcitabine. Front Pharmacol 8, 155. DOI: 10.3389/fphar.2017.00155

O’Hagan S, Kell DB (2017) Analysis of drug-endogenous human metabolite similarities in terms of their maximum common substructures. J Cheminform 9, 18. DOI: 10.1186/s13321-017-0198-y.

O’Hagan S, Kell DB (2017) Consensus rank orderings of molecular fingerprints illustrate the ‘most genuine’ similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates. bioRxiv version. bioRxiv 2017:110437.

O’Hagan, S. & Kell, D. B. (2016). MetMaxStruct: a Tversky-similarity-based strategy for analysing the (sub)structural similarities of drugs and endogenous metabolites. Front Pharmacol 7, 266. DOI: 10.3389/fphar.2016.00266

O’Hagan S, Kell DB. (2015). The apparent permeabilities of Caco-2 cells to marketed drugs: magnitude, and independence from both biophysical properties and endogenite similarities. PeerJ 3, e1405. DOI: 10.7717/peerj.1405. SYNBIOCHEM paper. pdf version. Preprint here. PMID 26618081.

O’Hagan S, and Kell DB. 2015. Software review: The KNIME workflow environment and its applications in Genetic Programming and machine learning. Genetic Progr Evol Mach, online. doi:10.1007/s10710-015-9247-3.

O’Hagan S, Kell DB (2015) Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites. Front Pharmacol 6, 105. DOI 10.3389/fphar.2015.00105SYNBIOCHEM paper.

O’Hagan S, Swainston N, Handl J, Kell DB (2015) A ‘rule of 0.5’ for the metabolite-likeness of approved pharmaceutical drugs. Metabolomics 11 (2), 323-339; DOI 10.1007/s11306-11014-10733-z. Minor erratum on p 340. PMID 25750602.

Dunn WB, Lin W, Broadhurst D, Begley P, Brown M, Zelena E, Vaughan AA, Halsall A, Harding N, Knowles JD, Francis-McIntyre S, Tseng A, Ellis DI, O’Hagan S, Aarons G, Benjamin B, Chew-Graham S, Moseley C, Potter P, Winder CL, Potts C, Thornton P, McWhirter C, Zubair M, Burns A, Cruickshank JK, Jayson GC, Purandare N, Wu FW, Finn JD, Haselden JN, Nicholls AW, Wilson ID, Goodacre R, Kell DB: Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics 2014:in the press.

Ellis, D.I., Cowcher, D.P., Ashton, L., O’Hagan, S. and Goodacre, R. (2013) Illuminating disease and enlightening biomedicine: Raman spectroscopy as a diagnostic tool. Analyst, 138(14), 3871-3884 doi:10.1039/C3AN00698K

O’Hagan, S., Knowles, J. & Kell, D. B. (2012). Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing. PLoS ONE 7, e48862.

Brown, M., Dunn, W.B., Ellis, D.I., Goodacre, R., Handl, J., Knowles, J.D., O’Hagan, S., Spasić, I. & Kell, D.B. (2005) A metabolome pipeline: from concept to data to knowledge. Metabolomics 1(1), 39-51.

O’Hagan, S., Dunn, W.B., Brown, M., Knowles, J.D. & Kell, D.B. (2005) Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal. Chem 77, 290-303.

Ellis, D.I., O’Hagan, S., Dunn, W.B., Brown, M. and Vaidyanathan, S. (2004) From genomes to systems. Genome Biology 5(11), 354.


I am a member of the Royal Society Chemistry (RSC).

After several years working in Hong Kong for various commercial laboratories as Laboratory  Technical Manager / Quality Assurance Manager / IT Manager, I returned to the UK in 2002, after a couple of jobs with Pfizer in Kent and Alcontrol Laboratories in Chester, I joined Prof. Kell’s research group in 2003. (When we were still part of UMIST).

My research interests include: Developing utilities and applications using various programming languages in the areas of :- Genetic and evolutionary programming; laboratory automation; analytical laboratory data analysis (including univariate and multivariate statistics); chemometrics; laboratory information management systems; machine learning, including deep networks; QSAR studies and computer modelling of (bio-)chemical systems.

I have some interest in programming in the following languages: Python; R; Java & Matlab.

Contact Details

If you wish to contact me, please use the following contact details:

E-mail: SOHagan@manchester.ac.uk

Address:School of Chemistry
The University of Manchester
Manchester Institute of Biotechnology
University of Manchester
131, Princess St
Manchester M1 7DN

Personal | Education | Publications | Miscellaneous | Contact