Moving beyond the hype: Building reliable AI for biopharma R&D
Large Language Models (LLMs) hold immense promise for accelerating drug discovery, but their adoption is severely hindered by two fatal flaws: a tendency to hallucinate and a fundamental lack of semantic intelligence. In high-stakes environments like drug target identification, these limitations can lead to costly dead ends.
In this recorded presentation from the Bio-IT 2026 Generative AI Track, Mark Hahnel outlines a practical, battle-tested path forward.
Discover how a neuro-symbolic approach—which marries the creative, probabilistic power of LLMs with the structured, verifiable logic of knowledge graphs—can transform standard AI models into dependable reasoning systems. You will learn how to ground your AI initiatives in truth, bridging the gap between internal discovery data and the global scientific research ecosystem.
Featured speaker
Mark Hahnel
Vice President, Open Research | Digital Science
Mark Hahnel is a leading voice in data visibility, open research, and the evolution of scientific knowledge management. At Digital Science, he focuses on empowering research institutions and commercial R&D teams to unlock the full pot
About the event
Originally presented on May 21, 2026, at the Bio-IT World Conference & Expo in Boston, MA. Interested in learning more about Digital Science’s solutions for life sciences? Contact our team.
