Recent advances in generative AI have transformed how we write code and work with language, and these same technologies are now reshaping biology and therapeutic research. AlphaFold demonstrates that deep learning can reliably predict protein structure from sequence, fundamentally changing structural biology. Building on this progress, biomedical foundation models, including IBM’s Biomedical Foundation Models such as MAMMAL and MMELON, apply generative and representation learning across diverse biological data types.
By learning reusable embeddings from large-scale datasets, these models support tasks such as molecule–target prediction and the design of therapeutic antibodies. In parallel, quantum computing has begun to show early but tangible progress in molecular simulation. While today’s hardware remains limited, quantum-centric workflows that combine classical simulation, AI-based surrogate models, and quantum experiments are already being used to study simplified molecular energies and interactions. These hybrid approaches are laying the foundation for future discoveries related to molecular structure that may scale beyond what is possible with classical methods alone. From a software perspective, the field is moving toward general-purpose biological models that function as platforms rather than single-use tools.
These models can be fine-tuned, combined with agentic systems, and integrated into existing ML pipelines to accelerate drug discovery, biomarker discovery, and experimental design. At the same time, accessibility to quantum technologies for the software community has grown through open frameworks such as Qiskit, lowering barriers to experimentation and development. Together, generative AI and quantum computing are establishing a new paradigm for therapeutic research: AI-driven discovery that delivers value today, alongside quantum methods being prepared to extend simulation power as hardware continues to mature.
Prof. Michal Rosen-Zvi is a senior researcher at IBM Research, serving as Director of Artificial Intelligence Technologies for Healthcare and Life Sciences. She is also an Adjunct Professor at the Faculty of Medicine at the Hebrew University of Jerusalem, and is involved in initiatives promoting the field, such as the 8400 initiative and the Israeli Society for High-Tech in Medicine (affiliated with the Israeli Medical Association).
Michal has followed an unconventional career path: she grew up as the daughter of a physician but chose not to follow in his footsteps, earning her bachelor’s, master’s, and PhD degrees in Physics. She later shifted into the world of technology and completed a postdoctoral fellowship in Artificial Intelligence in the United States (UC Berkeley, UC Irvine). She comes from a family of nine siblings, all raised in this environment. Today, she has come full circle, with her research primarily focused on applying AI tools to improve medicine and life sciences.
For years, she has studied the use of machine learning and artificial intelligence in healthcare, including: data analysis to generate medical insights; algorithms that help radiologists interpret imaging based on data analysis; algorithms that support physicians in diagnostic decision-making; and, more recently, AI models that identify new uses for existing drugs or accelerate drug development through simulations of clinical trials and the identification of candidate proteins in the drug development process.
Michal has published dozens of papers in scientific journals and led a subcommittee on medical innovation within the Digital Health Council, aiming to ensure that family physicians—not only researchers—use AI tools in patient care. Since 2025, she has served as Chief Scientist for the collaboration between IBM and the Cleveland Clinic, where an IBM quantum computer has been deployed. The joint teams have already demonstrated groundbreaking applications, including research into protein structures.
Keynote session: Hadas Grossmon Ella
Break
Lightning talks session
Roundtable closing
Talk by Hila Paz
Talk by Dr. Moran Mizrahi
Closing remarks
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