While AI promises to revolutionize drug discovery, 52% of life science professionals cite poor data quality as their main barrier. Without FAIR data principles, AI models remain untraceable “black boxes” prone to hallucinations. Learn how to ground your drug discovery and R&D in a verifiable, machine-actionable knowledge foundation. What you can get from the whitepaper: A FAIR Metadata Blueprint: Practical strategies to transition from unclear data and copyright policies to a traceable, audit-ready data foundation. Data Transformation Tactics: Actionable steps to successfully break down information silos and transform fragmented research data into connected, usable knowledge graphs. Hallucination Mitigation: Approaches to anchoring Large Language Model (LLM) outputs to structured, verifiable scientific evidence. Regulatory & Compliance Readiness: Guidelines for establishing absolute data lineage to meet strict compliance standards in pharma drug discovery
