Quantitative structure transcriptional activity relationship (QSTAR) - drug design based on bioassays

Activity: Talk or presentationInvited talkunknown

Description

Currently, the pharmaceutical industry faces a large percentage of compounds that fail to reach the market. To reduce the risk of such failures, modern molecular biology technologies together with computational “in silico” techniques support decision-making in the early stages of drug discovery. Toward this end, drug designers interrelate and interpret transcriptomic, genetic, proteomic, metabolomic, and assay data and, thereby, complement established methods in computational chemistry. As computational tools are emerging as key technologies in drug design, there is a need for integrating knowledge and experiences from the fields of bioinformatics, chemoinformatics, machine learning, and statistics into the area of drug design. The current practice in many pharmaceutical companies is to screen their huge compound libraries for biologically active compounds using disease- or target-specific bioassays (HTS assays). Thereafter, active compounds are optimized to increase on-target effects and simultaneously to decrease off-target effects using bioassays and 'omics data. This optimization relies on pharmacophores (target-specific functional groups) and structure-activity relationship (QSAR) models to represent the relationship between properties of a compound and its biological activity. I will present a current project on early stage drug design that incorporates gene expression and bioassays. We search for genes and gene modules that are differentially expressed after adding compounds to particular cell lines. Then we relate these genes to bioassay measurements of the same compounds. Next we determine the biological activity of compounds using gene expression, bioassays and their relations. Finally, compounds are optimized based on their biological activity, where, in particular, possible side effects are identified.
Period03 Oct 2012
Event title1st Austrian Symposium on Biomarker Development 2012
Event typeConference
LocationAustriaShow on map

Fields of science

  • 106005 Bioinformatics
  • 305 Other Human Medicine, Health Sciences
  • 102018 Artificial neural networks
  • 102 Computer Sciences
  • 106041 Structural biology
  • 101029 Mathematical statistics
  • 106023 Molecular biology
  • 106013 Genetics
  • 106002 Biochemistry
  • 102001 Artificial intelligence
  • 101004 Biomathematics
  • 102015 Information systems

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Nano-, Bio- and Polymer-Systems: From Structure to Function