DescriptionThe traditional drug discovery mainly relies on recombinant systems and simple second
messenger assays to profile compounds prior to testing in models of disease. Currently drug discovery is hindered by the fact the majority of compounds (52 %) fail in clinical trials due to lack of efficacy in humans there is a need to alter current practices. The huge translational problem are characterized by the fact that though classical second messenger assays in more disease relevant cells are more translational to human disease, however these assays are acute, and diseases such as IPF, asthma, COPD, Parkinson, Alzheimer are chronic
diseases characterised by long-term changes. Based herein more sophisticated early drug discovery platforms are required to decipher the mode of action of novel drugs in space and time to link it more closely to the disease setting. Such novel platforms require the analysis of complex data from advanced imaging and analytical techniques to understand biological structures and processes, with particular interests in the analysis of single-molecule imaging experiments, automated analysis of mass spectrometry measurements, and image reconstruction methods for diffuse optical imaging. Usage of a wide range of computational approaches to address these complex interdisciplinary problems, drawing on ideas from classical image processing, machine learning, computer vision, and topology are required. Based on this background, the symposium will recapture state-of-the-art early drug discovery platforms with a special focus on areas of artificial intelligence.
- Localized signaling