Christian Doppler Laboratory for Molecular Informatics in the Biosciences (CD-Lab MIB)

In recent years, molecular informatics has transformed from a niche discipline into a driving force of the research and development of functional small molecules such as drugs and agrochemicals. Advanced algorithms as well as powerful computer hardware are now opening unprecedented opportunities for the targeted design of safe and efficacious small molecules. However, the full potential of computational methods in the biosciences is by far not exploited yet. One of the main reasons for this situation is the fact that the most powerful technologies in molecular informatics, machine learning and simulations in particular, depend on the availability of substantial amounts of high-quality data for development and validation. Despite recently launched initiatives to boost collaborative research and learning, the vast majority of high-quality chemical, biological and structural data remain behind corporate firewalls, inaccessible for research by experts in academia. This initiative for the Christian Doppler Laboratory for Molecular Informatics in the Biosciences seeks to push the frontiers of machine learning and molecular dynamics simulations technologies for the prediction of small-molecule bioactivity by supporting three expert academic research groups of the University of Vienna and the University of Natural Resources and Life Sciences (BOKU) with big data on the chemical and biological properties of small molecules, and with significant capacities for experimental testing and method validation. The unique synergy that will be generated by this consortium stems from two important factors: First, the two industry partners of this consortium have strong interest in cheminformatics but their business areas are non-competing. Second, and from a scientific point highly important, these industry partners focus on distinct chemical spaces, opening a unique opportunity for academics to boost the capacity and applicability of in silico methods with uniquely diverse, high-quality data.