Developing a drug costs in the order of a billion USD and takes some 10 years. Drug repositioning tries to reduce cost and time by applying drugs that are already on the market to new uses. With the avalanche of publicly available data on compounds, genes, proteins, and diseases and their relationships, there is a tremendous need for computational approaches to organize and analyze this data and finally predict novel uses. In our group we are specifically focusing on developing algorithms and analysis pipelines using networks, protein structures, text-mining and ontologies for this purpose. We currently focus on pancreas cancer as application, where we have a wealth of experimental data from collaborations with the medical faculty.
To identify novel HSP27 inhibitors, we developed screening algorithms, which identify remotely similar binding sites across evolutionary unrelated target proteins. We screen the entire known structural proteome and predicted and experimentally validated novel HSP27 inhibitors. We are currently testing the compounds in vivo.
Generally, the avalanche of publicly available data on compounds, genes, proteins, and diseases and their relationships creates a tremendous need for computational approaches to organize and analyze this data. In my group we are specifically focusing on developing algorithms and analysis pipelines using protein structures and sequencing data. We currently focus on cancer as application, where we have a wealth of experimental data from collaborations with medical groups.