Heriot-Watt University Featured PhD Programmes
Birkbeck, University of London Featured PhD Programmes
King’s College London Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
Monash University Featured PhD Programmes

Computational and bioinformatic techniques for compiling potential drugs candidates for repositioning

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr K McGarry
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

This project is concerned with drug repositioning, this is where an existing drug may be deployed for a disease that is unrelated to its original target condition. The most often cited example is the drug developed by Pfizer (sildenafil) which was intended to treat angina by relaxing the coronary arteries and therefore allowing greater blood flow. This drug was discovered to have an interesting side-effect on male participants and was later marketed as Viagra, the drug now has annual sales of $1.6 Billion.
Drug repositioning is a suitable application area for computational intelligence because numerous online databases containing technical information on drug targets, protein interactions, side-effects and biological knowledge are freely available. Thus in-silico analysis can be used as a useful first stage to screen potential candidate drugs for possible redeployment. Within this PhD I take the position that drugs with side-effects are potential candidates for use elsewhere; it is a case of identifying potential diseases that may benefit from this re-deployment. The system would probably use graph based computational techniques to analyze drugs with known side-effects and compare the proteins involved in these side-effects with proteins known to be identified with other diseases.

The aim of this PhD would be to identify possible drugs that may be candidates for deploying against other diseases by using computational techniques to score and rank the candidate drugs. The student would NOT be expected to have a deep knowledge of chemistry or biology but a willingness to understand the bioinformatics and computational techniques that could be used, and to become familiar with the databases and the ability to program.


U. Daniel and K. McGarry, Data Mining Open Source Databases for Drug Repositioning using Graph Based Techniques, Drug Discovery World, Vol 16, issue 1, pages 64-71, 2015.
K. McGarry and U. Daniel, Computational Techniques for Identifying Networks of Interrelated Diseases, The 14th UK Workshop on Computational Intelligence, UKCI-2014, Bradford, Uk, 8th-10th Sept, 2014.
Jihong Yang, Zheng Li, Xiaohui Fan, and Yiyu Cheng, Drug–Disease Association and Drug-Repositioning Predictions in Complex Diseases Using Causal Inference–Probabilistic Matrix Factorization, Journal of Chemical Information and Modeling 2014 54 (9), 2562-2569
Barabasi, A., Gulbahce, N., Losalzo, J. (2011) Network Medicine: a network based approach to human disease. Nature Review Genetics, 12, 56-68
Cline, M., et al - Integration of biological networks and gene expression data using Cytoscape, Nature Protocols 2, 2366 - 2382 (2007), doi:10.1038/nprot.2007.324
McGarry, K.

How good is research at University of Sunderland in Computer Science and Informatics?

FTE Category A staff submitted: 13.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

FindAPhD. Copyright 2005-2019
All rights reserved.