Join us in Boston for an exclusive Neo4j GraphTalk Pharma & Life Sciences event — a private gathering of industry leaders, data pioneers, and graph technology experts exploring the transformative potential of Generative AI in the Pharma & Life Sciences space.
Discover how Neo4j’s powerful graph technology is driving innovation across the industry, from accelerating research and enhancing biomedical data intelligence to enabling explainable AI applications. Through real-world success stories, you'll gain practical insights into how graph-powered data reveals hidden connections, fuels smarter decision-making, and unlocks competitive edge.
Whether you're a data scientist, researcher, or innovation leader, this event offers a unique opportunity to network with peers and explore how graph technology is reshaping the future of life sciences.
Why attend?
- Connect with peers and pioneers driving data innovation
- Learn how graphs are solving some of the most complex challenges in the Pharma & LifeSciences industry
- See how explainable AI built on connected data can accelerate R&D and regulatory readiness
Dr. Jim Webber, Chief Scientist, Neo4j
AI agents in life sciences start every session from scratch, missing the "tribal knowledge" that tells researchers not just what worked, but why other approaches didn't. Neo4j Chief Scientist Dr. Jim Webber introduces the Context Graph: a memory architecture that combines an Enterprise Knowledge Graph, a Conversational Graph, and a Decision Graph so agents can reason about both success and failure. See how Merck uses this approach to power autonomous agents that collaborate on drug discovery, with every decision transparent, traceable, and grounded in clinical evidence.
Jonathan W. Lowe, Founder, Amalgo LLC
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.
From Patient Journey to Value: Applying Data, AI, and Knowledge Graphs in Pharmaceuticals & Life Sciences
Sook Hopkins, Principal Data Scientist, Nestle
As organizations advance their digital transformation journeys, connecting fragmented data is essential to creating a more complete understanding of stakeholder experiences. This session explores how data, AI, and knowledge graph technologies can help create a more connected view of patient and healthcare professional journeys, generating actionable insights that improve decision-making and deliver measurable business value. Drawing from real-world applications, the discussion will focus on how connecting data across the value chain can uncover meaningful opportunities and translate digital innovation into actionable impact.
Hamza Farooq, CTO, BioBox Analytics
Most teams have plenty of data. But when it lives in disconnected systems, decisions end up made from flattened summaries and spreadsheets. The evidence is out of reach, and the reasoning is lost the moment the meeting ends. This talk shows what becomes possible when that changes. BioBox is a decision layer that encodes scientific frameworks and institutional knowledge directly against a team's own data, making every prioritization transparent, repeatable, and explainable. Making this work requires data that is truly connected, which is why it runs on a Neo4J knowledge graph of entities, evidence, and provenance. We'll walk through how it's modelled, how it's queried, and how to track the way decisions evolve as new evidence arrives. The goal of: "What should we do next, and why has that changed?" becomes a question you can actually answer.
