Projects

REMEDi4ALL aims to make a significant leap forward in drug repurposing. This promising approach to drug development, consisting of identifying, testing, and validating new therapeutic indications for existing medications, is a developing field but faces numerous barriers and systemic inefficiencies. Still, its potential to significantly bring down times and costs of drug development - it focuses on already approved, discontinued, shelved or investigational therapeutics - makes this novel strategy attractive for rare and neglected conditions, cancer, emerging public health threats such as COVID-19 or new drug combinations. It also translates into more sustainable health systems.

We aim to validate and commercialise biomarkers and the resulting products for affective disorders to address the many unmet needs along the entire disease trajectory.

The goal of the AIOLOS project (Artificial Intelligence Tools for Outbreak Detection and Response) is to develop a digital platform to allow for early detection of new respiratory pathogens epidemics, monitor their spread, and inform decision-makers on appropriate countermeasures. AIOLOS will provide insights for private and public decision-making in a web-based dashboard, which leverages real-time data from multiple data sources, advanced artificial intelligence (AI), and predictive modeling.

To develop hardware such as microprocessors, field-programmable gate arrays (FPGAs) are typically used. However, optimizing the interconnection of these logic devices is itself a difficult problem for Turing machines (the so-called Quadratic Assignment Problem or QAP). Quantum or quantum-inspired hardware seems to be a better way to solve this problem. The approach in the QuantumQAP project is to combine quantum hardware and theoretical solution approaches that are applied in the two application fields of FPGA interconnection and post-quantum cryptography.

One approach in the context of the energy transition is to also feed green hydrogen into the existing German network for gas transport. In order to be able to plan, control and analyze such scenarios, existing simulation models must be extended so that they can represent gas mixtures with high hydrogen content. In addition, the coupling to the relevant power grids must be advanced. These are the goals of the TransHyDE-Sys project. This project is part of the BMBF flagship project TransHyDE.

The European project DIGIPD, funded with around 1.6 million euros and coordinated by the Fraunhofer Institute for Algorithms and Scientific Computing SCAI, is investigating the extent to which digital techniques (sensors, speech recognition, recognition of facial expressions) can be used to make a more precise and individualized diagnosis and prognosis of Parkinson's disease. The project is funded by the European network for personalized medicine, ERA PerMed, in the "Joint Transnational Call 2020". The German share of DIGIPD is funded by the Federal Ministry of Education and Research (BMBF).

The zet-o-map project aims to develop better tools for the identification of teratogenic compounds. One of the most promising assays is the zebrafish embryo teratogenicity assay (ZETA). In human risk assessment the evaluation of developmental toxicity or teratogenicity requires the testing of rodents (preferably rats) and non-rodents (preferably rabbits) as described in the OECD 414 guideline. Currently human safety assessment is undergoing a paradigm shift moving towards mechanistic risk assessment and there is a high demand to replace, reduce or refine animal tests where possible for ethical and economic reasons. zet-o-map is funded by the Long Range Research Initiative (LRI) of the European Chemical Industry Council (CEFIC).

The Fraunhofer project HABICHT aims at the development of a highly efficient electric motor for compressors in hydrogen fuel cells.

Biomedical knowledge graphs play a central role in big data integration. Bringing unstructured text into a structured, comparable format is one of the key assets. As cause and effect models, knowledge graphs can potentially facilitate clinical decision making or help to drive research towards precision medicine. Data and Knowledge Management, sometimes also called Information Management, is a core topic of Data Science. It is also a interdisciplinary field touching economics (how efficient and expensive is the solution?), psychology (do people use this solution in a way that was intended?) and of course computer science. Our aim is to build sustainable data infrastructure for biomedical data, personalized medicine, drug repurposing, reproducible AI and knowledge discovery.

The vision of VMAP analytics is to enable the realization of smart digital twins for materials.