
Agentic Approach: Pharma and Life Sciences Edition
Pharmaceutical and biotech organizations are drowning in data yet starving for insights. While AI promises to unlock breakthroughs, traditional approaches struggle to connect fragmented information across patents, publications, omics datasets, and clinical trials. The gap? Existing systems can't autonomously reason across these interconnected data sources to answer the complex, multi-step questions scientists actually ask.
Agentic AI changes the game by enabling autonomous agents to navigate, analyze, and synthesize information intelligently, but only when built on the right foundation.
Neo4j's graph database provides this critical knowledge layer, transforming disconnected data silos into a unified knowledge graph that agents can traverse to uncover hidden relationships and accelerate discovery.
Now is the time to act. As nine of the top ten pharmaceutical companies already leverage Neo4j, early adopters of agentic architectures are gaining competitive advantages in drug discovery speed, patent intelligence, and research efficiency.
You'll discover:

Dr. Alexander Jarasch is Neo4j’s Global Head of Pharma & Life Sciences, the graph database and analytics leader. With deep expertise in machine learning, and data engineering, he pioneers the integration of generative AI with knowledge graphs for drug target identification, competitive intelligence, and supply chain optimization in an industry where nine of the top ten pharmaceutical companies trust Neo4j.
Drawing on his bioinformatics foundation, Dr. Jarasch's career bridges chemistry, biotechnology, pharmaceuticals, and information technology. His innovative work applying knowledge graph analytics to combat diseases like diabetes has earned multiple industry awards. Before joining Neo4j, he served as Head of Data and Knowledge Management at the German Center for Diabetes Research (DZD), with earlier positions at Roche Diagnostics and the Max Planck Institute for Biochemistry.

Andreas Kollegger is the GenAI lead for developer relations at Neo4j. He has helped shape the graph database category over 15 years, contributing to the query language, tooling and design patterns that take graphs from a simple idea to a powerful solution.
Within AI, Andreas translates leading research papers into actionable implementations, both for developers pushing the boundaries and informing Neo4j product direction. In addition to public speaking and live workshops, he has published two courses on deeplearning.ai for GraphRAG and Agentic Knowledge Graph Construction.