Dr Zied Hosni

BSc (INSAT), MSc (INSAT), PhD (Glasgow)

Information School

Research Associate

A photo of Zied Hosni
z.hosni@sheffield.ac.uk
+44 114 222 2674

Full contact details

Dr Zied Hosni
Information School
Regent Court (IS)
211 Portobello
Sheffield
S1 4DP
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I finished my PhD in the Cronin Group at the University of Glasgow before securing a postdoctoral position in the Bioinformatics Hub in the Centre for Virus Research in Glasgow. During my research experience, I developed the ability to apply my knowledge of computational chemistry and synthesis into practical use in drug discovery, including machine learning tools for polymorphism predictions, artificial intelligence solution development, big data technologies and virtual screening.

Before coming to Sheffield, I was a research associate in the Centre of Computational Chemistry at Bristol University where I was investigating mechanistic insights of the stereoselectivity in boron-lithium chemistry. I was previously a research associate at Strathclyde Institute of Pharmacy and Biological Sciences at Strathclyde University (UK) where I fully utilised my machine learning for scientific projects, whilst collaborating with several global pharmaceutical companies such as Lilly, AstraZeneca and Novartis, providing the opportunity to liaise with industry professionals and experts.

Research interests

A key part of my project experience includes detailed investigation of biological properties of organic molecules and structures, utilising QSAR approaches, advanced statistical and machine learning such as Neural Networks, Deep Learning, Support Vector Machine and Random Forests. I exploited these methods of data mining to study the solubility and the biological activity of furadantine derivatives as drugs candidates for breast cancer. I used also Python and SQL to develop programs capable of ranking residues’ importance in a protein based on their non-covalent bonds and aiming to identify areas of the protein that ensure the stability and the functionality of this molecule.

I understand the DFT approach and have utilised this to investigate the kinetic and thermodynamic effects of solvents on the stabilisation of peptide bonds. I completed a project which involved searching for the transition states of molecules and its sensitivity to the nature of the solvents. This involved several programs such as ChemAxon, Knime, MOE, Hyperchem, Materials Studio, Schrodinger, Gromacs and Gaussian. Furthermore, I am experienced in computational simulations for the encapsulation of anticancer drugs into carbon nanotubes and their interactions with the DNA using both DFT and semi-empirical calculations.

I have used methods of the machine learning tools such as Support Vector Machine and the Naïve Bayesian algorithms to build predictive models of the biological activities from open-source databases. I also have 2-years’ experience using hybrid methods (QM/MM, and molecular dynamic studies for DNA-anticancer and antibodies-antigens interactions). I am familiar with the TDDFT calculations for the electronic investigation of organometallic compounds.