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Tuesday 26 January | 10:00 – 16:00
*** This session is not open to everyone. It is an invitation only event ***
Sponsored by:
Turning AI Potential into Measurable Impact Across Drug Discovery
The PharmAI Leaders’ Exchange brings together 25-30 senior leaders from pharma and biotech for a highly interactive, discussion-led forum focused on the practical realities of AI adoption across the drug discovery value chain.
Rather than showcasing technologies or success stories, this closed-door exchange is designed to facilitate candid peer-to-peer conversations on the organisational, scientific, and data challenges that must be solved to move from promising pilots to sustainable impact. Participants will work together to identify practical frameworks, operating models, and measurable approaches that can accelerate AI-enabled innovation while ensuring scientific rigor, regulatory confidence, and business value.
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10:30 Autonomous and Agentic Drug Discovery: Designing the Next Generation of Discovery Workflows
AI is moving beyond helping with individual tasks and is beginning to support entire research workflows. This session will explore what agentic AI could realistically do in drug discovery and what organisations need to do to prepare for it.
Discussion topics:
- What parts of drug discovery could AI automate in the next few years?
- Which decisions should always stay under human control?
- Where can AI safely make recommendations or take action on its own?
- What skills, technology, and processes do organisations need to prepare for agentic AI?
- How realistic is fully autonomous drug discovery, and what are the biggest challenges?
- Who is responsible when an AI system proposes or carries out a new experiment?
Sam Cooper, Director, Data/ML, Engitix Therapeutics
11:30 What Does “Good Data” Actually Mean for AI
AI is only as good as the data it is trained on. To use AI effectively across drug discovery and development, organisations need high-quality data, clear standards, and good collaboration between scientific and data teams. This session will explore what makes data ready for AI and how better data can lead to better outcomes.
Discussion topics:
- What makes data suitable for AI?
- What data standards should organisations have in place?
- Which types of data should companies invest in first?
- Who should be responsible for managing and improving data quality?
- How can clinical and patient data help improve future AI models?
- How can organisations use data responsibly while protecting privacy and patient trust?
Elif Ozkirimli, Head of Computational Science Products, Roche
13:30 Moving Beyond Pilots: How Do We Prove AI Is Delivering Value?
Many organisations have tested AI in small projects, but fewer have seen a clear impact on R&D performance or business results. This session will explore how companies measure success, demonstrate value, and decide when to scale AI across the organisation.
Discussion topics:
- How do we know if an AI project has been successful?
- What should we measure when testing AI in R&D?
- When is AI most useful, and when are people still better?
- How can small AI projects lead to bigger business improvements?
- What does a good return on investment (ROI) for AI look like?
- Why do some promising AI projects fail to scale?
- How can organisations benchmark their AI progress against others?
Karen Sayal, Translational AI Innovation Lead, UCL Hospitals NHS Foundation Trust
14:30 Building the Competitive AI Advantage: Talent, Culture, and Strategic Capability
As AI becomes a bigger part of drug discovery and R&D, biopharma are rethinking how teams are built, trained, and supported. This session will explore the skills people need, the new roles that are emerging, and how companies can help their workforce use AI effectively.
Discussion Topics:
- What new roles and capabilities are emerging as AI becomes a core component of pharmaceutical R&D?
- How do we create organisations where every discovery scientist is AI enabled?
- What is the right balance between central AI platform teams and embedded computational experts?
- Which capabilities will differentiate the leading AI-first biopharma organisations by 2030?
Adrian Freeman, Executive Director, Head of Bioanalytical BioPharma Development, AstraZeneca