Catalog of previous student projects.
The search for new drugs is a very challenging and
costly endeavor. The possibility of using computational
methods for screening compounds at an earlier stage can
significantly improve the success rate among drug candidates,
as many late drug failures due to toxicity and other
factors thus can be avoided.
Chemoinformatics is a
rapidly growing field with a large application potential in the pharma
industry. At CBS, academic research in this area was established in January 2005 as a joint project between the Technical University of Denmark (DTU) and the Pharmaceutical Division at Copenhagen University (KU-FARMA), supported by the NABIIT initiative under the Danish Council for Strategic
Research and from the Danish Technical Research Council.
The Chemoinformatics Group at CBS works with
the development of new and innovative computational
tools for use in the drug discovery and optimization
process. The research is presently focused mainly on
analysis of large compound and property databases, and
the development of predictive tools using machine learning
and computational chemistry methods. Such models
are based on the structural features of the drug molecules,
combined with relevant biological and chemical
information in such a way that it becomes possible to
predict the behavior of unknown compounds.
Examples of current research projects are:
An integrated part of this research effort
is building an in-house infrastructure of accessible
data by collecting a number of relevant compound databases
and data sets. New links between chemoinformatics,
bioinformatics and systems biology are also explored.
of pre-screening methods used for selecting compounds
for a drug discovery pipeline,
- prediction methods for
properties like solubility and various types of toxicity,
- prediction of drug toxicity based on NMR metabonomics
data from rat urine, and
- modeling of hERG ion
The group also organizes and is responsible for the *NEW* CBS Chemoinformatics in Drug Discovery course (masters/PhD level), starting September 7, 2006 and is thus leading the way in providing education in this field. Course poster 2007.
Full length list of CBS publications
- Prediction of pH-dependent solubility of Histone Deacetylase (HDAC)inhibitors, Kouskoumvekaki I, Hansen NH, BjÃ¶rkling F, Vadlamudi SM and JÃ³nsdÃ³ttir SÃ, SAR and QSAR in Environmental Research, 19, 167-177, 2008
- A hERG Classification Model based on a Combination of Support Vector Machine Method and GRIND Descriptors, Li Q, JÃ¸rgensen FS, Oprea T, Brunak S and Taboureau O, Molecular Pharmaceutics, 5, 117-127, 2008
- Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification, Kouskoumvekaki I, Yang Z, JÃ³nsdÃ³ttir SÃ, Olsson L, Panagiotou G, BMC Bioinformatics, 9:59, 2008
- Kemoinformatik: LÃ¦r computeren at finde nye lÃ¦gemidler, Kouskoumvekaki I, Hansen NT, JÃ³nsdÃ³ttir SÃ, Dansk Kemi, 88, 23-25, 2007 (Also printed in Biozoom, 10, 19-23, 2007.)
- Monitoring novel metabolic pathways using metabolomics and machine learning; induction of the phosphoketolase pathway in Aspergillus nidulans cultivations, Panagiotou G., Kouskoumvekaki I, JÃ³nsdÃ³ttir SÃ and Olsson L, Metabolomics, 3, 503-516, 2007
- Prediction of pH-Dependent Aqueous Solubility of Druglike Molecules,
Hansen NT, Kouskoumvekaki I, JÃ¸rgensen FS, Brunak S and JÃ³nsdÃ³ttir SÃ, J. Chem. Inf. Model., 46, 2601-9, 2006
- Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates,
JÃ³nsdÃ³ttir SÃ, JÃ¸rgensen FS and Brunak S, Bioinformatics, 15;21, 2145-60, 2005.
- Improving the odds in discriminating drug-like from non drug-like
compounds, T.M. Frimurer, R. Bywater, L. NÃ¦rum, L. NÃ¸rskov
Lauritsen and S. Brunak, J. Chem. Inf. Comput. Sci., 40, 1315-1324, 2000