Adoption of AI for Early Drug Discovery: Challenges and Opportunities for CROs
AI is rapidly moving from experimentation to real-world application in early drug discovery. But where does it truly add value for CROs and what continues to limit broader adoption?
Join us for this valuable webinar to explore how AI is being applied across early discovery, from target and molecule prioritization to experimental planning and iterative learning across Design–Make–Test–Analyze (DMTA) cycles. The discussion will examine where AI is demonstrating measurable scientific value, how it complements expert judgment, and the practical challenges organizations face when integrating AI into discovery workflows.
Drawing on hands-on experience across multiple discovery environments, we’ll highlight operational and organizational considerations relevant to CROs, including data fragmentation, governance, workflow integration, and change management. You’ll learn about:
- Where AI is showing practical value across early discovery workflows
- How AI can support target identification, molecule prioritization, and DMTA cycles
- Key barriers impacting adoption and workflow integration
- Why scientific oversight remains critical to effective AI implementation
- Considerations for embedding AI into day-to-day discovery environments
Don’t miss key insights on how to approach AI adoption in early discovery and what it takes to integrate AI more effectively into scientific workflows. Register today!