AI holds immense promise in accelerating drug discovery and development—but the journey from data to therapeutic impact is far from straightforward. Despite revolutionary breakthroughs, challenges around data quality, translational relevance and clinical implementation still persist.
How can AI be harnessed to solve real-world problems in drug discovery and bring more targeted, effective therapies to patients faster?
By attending this webinar series, you will:
- Explore how and where AI is delivering tangible value in the drug discovery pipeline
- Understand why data quality and context are critical for AI model performance and impact
- Discover how AI is driving predictive biology and identifying new biomarkers and therapeutic targets
**Please note that by registering for the first webinar, you will automatically gain access to both sessions. If you’re unable to attend live, on-demand recordings will be available.**
Webinar 1: Where Are We Seeing Value in AI Implementation in Drug Discovery?
Wednesday 16th July 2025 at 3pm BST / 4pm CEST / 10am EST
Sponsored by Turbine
Join us for the first webinar to hear how leaders from biopharma are realizing real-world value from AI. Learn where AI tools are being most effectively applied, the challenges limiting wider adoption and the key capabilities needed to scale their impact in drug discovery.
Hear from:
- Guglielmo Iozzia, Director, ML/AI and Applied Mathematics, MSD
- Marta Milo, Director, Oncology Data Science, AstraZeneca
- Richard Bonneau, VP of ML for Drug Discovery, Genentech
- Elif Ozkirimli, Head of Computational Science Products, Roche
- Daniel Veres, Chief Scientific Officer & Co-Founder, Turbine
Webinar 2: Why Is Data the Most Critical Ingredient in AI-Driven Drug Discovery?
Wednesday 23rd July 2025 at 3pm BST / 4pm CEST / 10am ET
Data remains the single greatest determinant of success in AI-driven drug discovery. This session will explore how industry leaders are improving model accuracy and clinical relevance through novel data types, large-scale platforms, and integrative approaches.
Talk 1: Examining aspirations of large scale datasets and foundation models for drug safety evaluations
- Arijit Patra, Senior Principal Scientist, UCB
Talk 2: Engineering Cells with Foundation Models
- Bo Wang, Assistant Professor, University of Toronto
