3rd edition of GraphTalk pharma & life sciences returns on 2 July 2026 in Munich, again as a hybrid event.
Join us to explore how leading organizations are moving beyond AI experimentation and into production-ready, reliable AI systems powered by context graphs.
AI fatigue isn’t about the model, it’s about the foundation. In pharma and life sciences, fragmented data limits the impact of GenAI. Without connected, explainable context, AI remains stuck in pilots.
Knowledge graphs change that. They unify data across scientific literature, omics, clinical trials, and real-world evidence, creating a structured reasoning layer that enables grounded, traceable, and up-to-date insights.
This year’s agenda follows the pharma product lifecycle, showcasing how GenAI and knowledge graphs drive impact across:
- R&D – accelerating discovery and target identification
- Clinical – enabling explainable, data-driven decisions
- Supply Chain – improving resilience and operational intelligence
Throughout the day, you’ll hear real-world use cases from leading organizations using Neo4j to bring context to AI and move from pilots to production.
You’ll leave with a clear understanding of where GenAI and context graphs can take your organization next and how to get there.
Choose your preferred experience: attend physically on the day, or stream online, whatever suits your schedule and interest.
This is a co-located event with two dedicated tracks—Pharma & Life Sciences and Manufacturing—running in parallel. You’ll have the opportunity to network with peers across both industries and explore how graph technology is driving innovation at scale.
In this keynote, we will explore how knowledge graphs unlock the natural connections within enterprise data, providing the context AI needs to deliver meaningful results. He will highlight how organizations leverage graph-powered contextual intelligence to transform decision-making, turn data into competitive advantage, and drive real business impact.
Biomedical knowledge graphs (KGs) encode multi-scale biological relationships and serve as a powerful foundation for early drug discovery. In this talk, we present two KG-driven approaches for target hypothesis generation and prioritization. First, KG reasoning methods leverage graph topology and local neighborhood structure to infer novel gene-disease associations, augmenting curated knowledge with predicted connections . Second, graph-based retrieval-augmented generation (GraphRAG) leverages the KG as a structured retrieval source, anchoring each retrieved fact to a verifiable graph relation. This structured grounding improves factual reliability and reduces hallucinations compared to standard RAG. Together, they demonstrate how knowledge graphs can serve as both a predictive engine and a grounding mechanism for AI-supported target discovery.
Insel Spital Bern I From free text to Knowledge Graphs: visualizing Swiss medication and procedure mapping in Neo4j
We present a Neo4j-centered framework for harmonizing Swiss electronic health record medications and procedures. Heterogeneous, multilingual free-text entries were mapped to standard vocabularies using two medication workflows—OHDSI USAGI and a constrained large language model approach—and a cross-lingual retrieval pipeline for procedures. All mappings, classifications, and semantic relationships were ingested into Neo4j to support interactive graph visualization, validation, and gap analysis. The graph structure enabled exploration of semantic neighbourhoods, detection of inconsistent or missing links, and assessment of mapping quality through shortest-path metrics. Neo4j therefore served not only as a repository, but as an analytic and visual engine for clinical interoperability.
An industry standard for timely investigation closure is 30 days. We present a human-in-the-loop approach to reducing investigation times with AI and Context Graph data. (If you use tools like Veeva or TrackWise, your organization is already capturing the needed context.) Intuitively, lean manufacturing leaders know that shorter investigation timelines should benefit the bottom line, but where and how much? We quantify the financial value of shorter investigations and use knowledge graph simulations to avoid the bullwhip effect.
