Project No. 2316
Prof Paul Cox – University of Portsmouth
Prof John Spencer – University of Sussex
Dr Sassan Hafizi – University of Portsmouth
The main aim of this project is to use computer modelling methods to progress promising initial drug molecule ‘hits’ into lead candidates.
The work will utilise experimental data obtained from the Sussex Drug Discovery Centre (SDDC) and the School of Pharmacy & Biomedical Sciences at the University of Portsmouth.
Several different state-of-the-art computational methods will be used. These will include Scaffold and Fragment Replacement, Structure-Activity Relationship Exploration, Pharmacore Modelling, Molecular Docking, 2D and 3D Fingerprint Screening and 3D Pharmacophore Building. The methods used will be selected according to the experimental data available in each case. Key candidate molecules will be synthesised and tested at Sussex to validate the modelling work and to provide further experimental data to refine the models, if required.
A good example of the type of data available for this project is provided by our work on the TAM receptors (Tyro3, Axl and MerTK). These receptors are very promising targets found in abundance in the tumor microenvironment. Several initial hits for these targets have already been identified, as part of an ongoing collaboration between the PI and Co-Is (ScoBio funded), thus providing datasets ideal for detailed computational investigation. The recent availability of unbound structures for the human form of these kinase receptors also makes computational work in this area particularly timely. In addition to the potential for optimising therapeutic agents for the treatment of cancer, we will work with datasets from the SDCC and Portsmouth for other diseases with unmet medical needs. Current promising projects include novel DNA repair enzymes, bromodomains and p53 rescue. Hence, the project has significant potential for impact on the future health and wellbeing of society.
This work is ideally suited for someone with a background in biological sciences with a strong interest in both chemistry and the application of computational methods in drug discovery.