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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:
  • Development 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 channel blockers.
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.

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.

Selected Publications:
  • Prediction of pH-dependent solubility of Histone Deacetylase (HDAC)inhibitors, Kouskoumvekaki I, Hansen NH, Björkling F, Vadlamudi SM and Jónsdóttir SÓ, 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, Jørgensen 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, Jónsdóttir SÓ, Olsson L, Panagiotou G, BMC Bioinformatics, 9:59, 2008
  • Kemoinformatik: Lær computeren at finde nye lægemidler, Kouskoumvekaki I, Hansen NT, Jónsdóttir SÓ, 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, Jónsdóttir SÓ and Olsson L, Metabolomics, 3, 503-516, 2007
  • Prediction of pH-Dependent Aqueous Solubility of Druglike Molecules, Hansen NT, Kouskoumvekaki I, Jørgensen FS, Brunak S and Jónsdóttir SÓ, J. Chem. Inf. Model., 46, 2601-9, 2006
  • Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates, Jónsdóttir SÓ, Jørgensen 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. Nærum, L. Nørskov Lauritsen and S. Brunak, J. Chem. Inf. Comput. Sci., 40, 1315-1324, 2000
Full length list of CBS publications