Fraunhofer Institute for Translational Medicine and Pharmacology ITMP
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP

Data Science & Generative AI

Fraunhofer ITMP's Innovation Platform Data Science & Generative AI helps industrial partners transform data into measurable value. At our sites in Hamburg, Frankfurt, Berlin, Göttingen, and Penzberg, we develop robust analysis workflows and model-based tools for biomedical research, pharmaceutical development, diagnostics, and industrial applications.

Our focus is on continuous end-to-end processes: from reliable data management and reproducible analyses to scalable solutions that meet the highest industry and compliance standards. This enables companies to shorten development cycles, stabilize processes, and better utilize existing data potential.

Our approach accelerates innovation, reduces operational risk, and sustainably strengthens competitiveness in dynamic markets.

 

Core Expertise:

  • Integration of multimodal data (omics, process, imaging, clinical, chemical, biological, and experimental data)
  • Machine learning, deep learning, and development of foundation models for scientific and industrial applications
  • Generative AI for molecular design, synthetic data, text processing, and process optimization
  • Cloud-native infrastructures and scalable data engineering solutions
  • Expertise in regulatory-compliant, trustworthy, and explainable AI for sensitive research environments

 

Objectives/Services:

  • Development of scalable end-to-end data science workflows for research, diagnostics, and industrial processes
  • Design, training, and deployment of generative AI models for simulation, knowledge extraction, and multimodal data generation
  • Establishment of secure, compliant data ecosystems for cross-site collaboration and efficient data governance
  • AI-supported automation of laboratory, screening, and analysis processes to enhance efficiency and reproducibility
  • Strategic consulting, feasibility studies, and co-creation formats for data-driven innovation projects
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IDERHA

IDERHA is a European public-private partnership launched in April 2023. This pioneering project addresses the obstacles in accessing, integrating and analyzing health data to maximize their value for patient care and medical research. An open, disease agnostic, federated data space will be developed. The federated data space will enable connectivity, access, use and reuse of digital health data. In IDERHA, consensus policy recommendations on health data access and heterogeneous health research such as real-world evidence (RWE) are developed for regulatory and HTA decision making.

Partners: IDERHA is led by the Fraunhofer ITMP and Johnson & Johnson Medical GmbH, in a consortium of 33 academic, clinical, medtech, pharmaceutical, and IT partners, as well as patient advocacy organizations and public authorities, including Fraunhofer institutes SCAI and ISST.

Further Informations

SYNTHIA

SYNTHIA is an ambitious collaboration between public and private institutions to facilitate the responsible use of Synthetic Data (SD) in healthcare applications. The project will improve the methodological and technical aspects of SD Generation (SDG) by developing new techniques and advancing established ones for different data modalities, including genomics and imaging, to improve the generation of realistic multimodal and longitudinal data.

The open SYNTHIA federated platform will facilitate responsible SD use by the health research community, in particular long-term access to extensively validated, reusable synthetic datasets, as well as to SDG workflows and SD assessment frameworks. A multidisciplinary collaboration of SDG developers, FAIR data experts, clinical researchers, developers of therapies and data-based tools, legal experts, socio-economic analysts, regulatory, policy advocacy, and communication experts will provide a 360º vision on how to advance healthcare applications through SD use.

Partners: The consortium comprises 43 academic, clinical, pharmaceutical, IT and public partners, including the Fraunhofer Institute for Algorithms and Scientific Computing SCAI, the Fraunhofer Institute for Digital Medicine MEVIS and the Fraunhofer Institute for Translational Medicine and Pharmacology ITMP.

Further Informations

FAIRplus

The vast amounts of data generated in life science research have the potential to add to our understanding of disease and help advance drug development. Yet most data are hidden away in proprietary databases and stored in different formats. The goal of FAIRplus was to deliver guidelines and tools to facilitate the application of ‘FAIR’ principles to data from certain IMI projects and datasets from pharmaceutical companies. The project therefore makes it easier for other researchers to find the data and integrate it into their own research. The project also organized training courses for data scientists in academia, small and medium-sized enterprises (SMEs) and pharmaceutical companies. Ultimately, the project hopes to change the culture of data management in the life sciences sector.

Further Informations

Knowledge Graph Generator

The Knowledge Graph Generator (KGG) project, which has now been completed, developed an automated workflow for generating knowledge graphs for the life sciences, enabling a comprehensive representation of disease-associated entities such as proteins, signaling pathways, genetic variants, chemicals, mechanisms of action, assays and adverse effects.  By integrating curated resources including OpenTargets, UniProt, ChEMBL, the Integrated Interactions Database, and GWAS Central, KGG created FAIR-compliant, interconnected graphs that support complex scientific queries and downstream analyses.

The project demonstrated the practical value of knowledge graphs in translational and application-focused research. Use cases included identifying shared molecular entities to explore comorbidities, discovering putative therapeutic targets, repurposing drug candidates for Parkinson’s disease, and assessing the drug-likeness of chemicals. These outcomes highlight KGG’s potential to accelerate industrial research and development, bridging preclinical findings with actionable insights for drug discovery and bringing innovation closer to market.

Resources and source code from KGG are publicly available for the research community: Further Informations

Weng J, Ju F, Lyu Z, Fan N, Smit DJ, Xu W, Wu X, Becker P, Xu Y, Schweiger MR, Hillmer AM, Harwig R, Gul S, Link A, Meder L, Fang N, Dong Q, Bruns CJ, Ren N, Zhao Y. Single-cell insights into tumor microenvironment heterogeneity and plasticity: transforming precision therapy in gastrointestinal cancers.
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Nat Rev Drug Discov. 2025;24:521–542.
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Kuzikov M, et al. "Experimental and machine learning-based exploration of repurposed drugs reveals chemical features underlying phospholipidosis."
Patterns. 2025;101453.
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Karki R, Gadiya Y, Zaliani A, Pokharel B, Babaiha NS, Ostaszewski M, Hofmann-Apitius M, Gribbon P. "KGG: a fully automated workflow for creating disease-specific knowledge graphs." 
Bioinformatics. 2025 Jul;41(7):btaf383.
doi: 10.1093/bioinformatics/btaf383