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Tuesday 27 January 2026 | 10.00am – 5.00pm
Brought to you in partnership with Hewlett-Packard
Shape the future of AI-driven drug discovery
AI promises to cut costs, reduce labour and prevent late-stage failures. Yet, as with any emerging technology, adoption comes with challenges, particularly in managing expectations during this transformative phase.
Join the PharmAI Leaders Exchange for a highly interactive and collaborative discussion. Each session will be guided by a moderator who will help steer the conversation and encourage participation.
This pre-day event is a rare opportunity to network, discuss your challenges, exchange ideas, and benchmark with your peers.
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10:30 Strategic AI Investment: From Proof to Impact
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Are we moving beyond proof-of-concept and delivering measurable ROI, leading to increased executive buy-in and scaled adoption of AI in drug discovery?
- Managing expectations: AI won't shrink timelines from 10 years to 2 years, but it can optimize decision-making, de-risk assets, and improve pipeline productivity. How do we separate hype from actionable opportunity?
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How to build and manage an AI investment portfolio across the R&D pipeline, (from early discovery to clinical validation) to ensure value at every stage.
Mani Mudaliar, Director, Quantitative Biomarkers, Recursion
11:30 Quantifying AI Success: KPIs That Matter
- How do we define and align AI-related KPIs with core business goals? Is success measured by user adoption, model performance, or validated clinical impact?
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Avoiding vanity metrics by shifting focus to meaningful outcomes like reduced failure rates, faster decision-making, and enhanced pipeline productivity.
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Where is the real value of AI in drug discovery and how do we track it?
Nikolina Nakic, VP & Head of Target Discovery, Novo Nordisk
14:00 Unlocking Innovation Through Pre-Competitive & Startup Collaborations
- Pre-competitive partnerships as a mechanism for risk-sharing, learning, and algorithm enhancement without compromising IP.
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How to effectively collaborate with startups. What makes partnerships succeed or fail? How to create win-win feedback loops?
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Building shared benchmarks, synthetic datasets or testbeds that accelerate algorithm validation across the ecosystem.
Victor Neduva, Head of Target Discovery & Disease Data, pRED, Roche
15:00 Training AI: Data, Metadata, and Strategic Feedback Loops
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Building AI-ready data products: Addressing fragmented, incomplete, or noisy datasets through better metadata, annotation and standardization.
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Creating robust feedback loops between AI developers and users to enhance algorithm performance and contextual relevance.
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How to prioritize datasets across therapeutic areas to align with high-value AI use cases (e.g. target identification and patient stratification).
Becky Saunders, Associate Director, Computational Biology and Data Science, Tangram Therapeutics
16:00 Building Future Ready AI Teams in Pharma
- As AI becomes core to R&D strategy, how are pharma organisation charts evolving? What new roles, hybrid skills and inter-team collaborations are emerging?
- How to effectively hire and develop professionals who understand biology, statistics and AI and how to scale their impact quickly.
- Upskilling within pharma constraints: What’s working, what isn’t, and lessons from the field.
- Centralized AI teams vs. embedded domain experts: How do you strike the right balance for innovation and operational efficiency?
- What critical skill sets will define success in AI-enhanced drug development over the next 5–10 years?
Marta Milo, Director of Computational Biology - Oncology R&D, AstraZeneca