ayout 1 - EMBL Grenoble

EMBL-EBI
Chemogenomics and drug discovery
Previous and current research
The human genome project has delivered huge potential for the discovery of novel therapeutics.
In sharp contrast to this potential and promise is the small actual translation of functional genomics
discoveries into clinically useful agents. The goal of our group is to understand the signalling,
regulatory and physicochemical characteristics of historically successful drug targets, and
to provide predictive methods to prioritise future potential drug targets.
Our lab relies of a series of public domain databases to perform its research, many of which have
been built and developed within our own group. These databases cover medicinal chemistry, clinical
development and launched drugs, and are known by the name ChEMBL. Additionally, all our
work is computational although we have collaborations with key experimental groups. Finally, we
apply a broad range of knowledge discovery from data (KDD), data visualisation, and predictive
methods in our work.
Key to the assessment of future drug targets is an integrative approach which can consider biochemical
binding data, alongside functional and systemic effects and also include protein structure
and binding site data. As such we apply a uniquely broad set of approaches from
cheminformatics, bioinformatics, homology modelling, docking and machine learning.
We are a newly established group at EMBL-EBI, following the award of a grant from the Wellcome
Trust to transfer our previous databases and research from the private to public domain.
John Overington
PhD 1991, Birkbeck College,
University of London.
Postdoctoral work at Imperial
Cancer Research Fund.
Manager, Molecular
Informatics Structure and
Design, Pfizer, Sandwich.
Senior Director, Molecular
Informatics, Inpharmatica.
Team leader at EMBL-EBI
since 2008.
Future projects and goals
We are interested in the informatics-based prioritisation of drug targets for application in the area of neglected diseases, in particular those
diseases caused by pathogenic organisms. We are also interested in data-mining approaches to allow semi-automated design and optimisation
of hit and lead chemical series using generalisation of systematic rules discovered in our databases. Finally we have a strong interest in
the application of these techniques for newer classes of therapeutics such as monoclonal antibodies (mAbs) and non-human secreted proteins
(for example, helminth proteins that naturally suppress immune response in the host organism).
Selected references
Agüero, F. et al. (2008). Genomic-scale prioritisation of drug targets:
TDRtargets.org. Nat. Rev. Drug. Discov., 7, 900-907
Hopkins, A.L., Mason, J.S. & Overington, J.P. (2006) Can we
rationally design promiscuous drugs? Curr. Opin. Struct. Biol., 16,
127-136
Overington, J.P., Al-Lazikani, B. & Hopkins, A.L. (2006) How many
drug targets are there? Nat. Rev. Drug Discov., 5, 993-996
Danilewicz, J.C. et al. (2002). Design and synthesis of thrombin
inhibitors based on the (R)-Phe-Pro-Arg sequence. J. Med. Chem.,
5, 232-253
Mizuguchi, K. et al. (1998). HOMSTRAD: a database of protein
structure alignments and homologous families. Protein Science, 7,
269-271
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