Computational Health Center Institute of AI for Health
The Institute of AI for Health (AIH) develops solutions for digital health challenges. As an emerging European hub for applied artificial intelligence (AI) and machine learning (ML), we model disease kinetics, develop new drugs and treatments, analyze biomedical images, and integrate multi-omics data for precision medicine. Join us to drive biomedical discovery and build solutions that transform the future of medicine.
The Institute of AI for Health acknowledges generous funding from the High-Tech Agenda Bayern.
The Institute of AI for Health (AIH) develops solutions for digital health challenges. As an emerging European hub for applied artificial intelligence (AI) and machine learning (ML), we model disease kinetics, develop new drugs and treatments, analyze biomedical images, and integrate multi-omics data for precision medicine. Join us to drive biomedical discovery and build solutions that transform the future of medicine.
The Institute of AI for Health acknowledges generous funding from the High-Tech Agenda Bayern.
Helmholtz MunichJuly 2025 Symposium - Generative AI in Molecule Discovery
The Munich Generative AI in Molecule Discovery Symposium will explore recent advancements in generative AI models for molecule discovery, with a focus on applications in health and drug development.
Topics include AI-driven design of small molecules, peptides, and proteins, drug repurposing, and AI-based modeling of DNA and RNA sequences.
We aim to address both the opportunities and challenges in applying generative AI to these critical areas of research over this day-long event.
All participants have to register (including internal) until 24th of June.
Publications
Gottschlich A, Thomas M, Grünmeier R, […], Marr C*, Kobold S*. 2023. Single-cell transcriptomic atlas-guided development of chimeric antigen-receptor (CAR) T cells for the treatment of acute myeloid leukemia. Nature Biotechnology (2023)
Matek C, [...], Marr C. 2021. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.Blood 138, 1917–1927
Science Management
