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Bio-IT World Conference & Expo 2026

The world’s premier event showcasing the technologies and analytic approaches that solve problems, accelerate science, and drive the future of precision medicine.

19th – 21st May, 2026

Boston

United States

We’re looking forward to the BioIT World Conference & Expo and the pre-conferene Knowledge Graph Symposium, coming up on May 19-21 in Boston, MA, United States, where metaphacts, part of Digital Science, is a Gold sponsor!

Opportunities to connect with us

At our presentation in the Generative AI Track

A Practical Path to AI-Grounded Drug Discovery with Knowledge Graphs
May 21, 12:55 pm EST, Mark Hahnel, Vice President, Open Research, Digital Science

LLMs in drug discovery suffer from hallucination and lack of semantic intelligence. This talk presents practical, knowledge-grounded AI applications using knowledge graphs for use cases like target identification and polypharmacy prediction. The core is the neuro-symbolic approach which uses a knowledge graph to transform probabilistic LLMs into reliable reasoning systems. Learn to accelerate your pipeline by connecting internal knowledge with the research ecosystem via persistent identifiers.

At our booth: You will find us on the event floor at booth 718 where we look forward to speaking with you, learning more about your use cases and discussing how you too can leverage the integration of knowledge graphs and LLMs to drive trustworthy and efficient insights and knowledge discovery.

At our presentation during the pre-conference Knowledge Graph Symposium

The Foundation for Trustworthy AI: From FAIR Data Principles to Enterprise-Ready AI in Pharma
May 19, 11:45 am EST, Mark Hahnel, Vice President, Open Research, Digital Science

Pharma faces a “scientific content crisis” due to poor data quality, non-FAIR data and governance gaps, hindering AI and regulatory compliance. This session offers an actionable blueprint: using FAIR principles for policy foundation and knowledge graphs for the operational layer. Attendees will hear strategic recommendations: investing in data infrastructure, adopting neuro-symbolic architectures to eliminate hallucinations, and building for machine actionability for audit-ready, trustworthy AI.