Explore how knowledge graphs integrate multi-source biological data, such as genetic, proteomic, and clinical information, into unified models that accelerate target discovery and disease understanding, with AI enhancing the extraction of actionable insights.
Learn how data normalization and the latest curation strategies ensure that biological datasets are clean, standardized, and AI-ready, enabling accurate analysis and improved model performance for drug development.

Hear cross-functional perspectives on successfully implementing AI across process development teams, from aligning with quality, IT, and manufacturing to overcoming cultural and technical barriers, with a focus on driving operational efficiency and long-term value.

Harnesses collaborative innovation networks to integrate external expertise and accelerate breakthrough AI development.

Dive deep into how large language models are automating complex planning tasks, from trial feasibility assessments and synthetic protocol generation to cross-functional alignment and regulatory-ready documentation, with real-world examples of scalable implementation and measurable impact.

• Learn how a score-based diffusion model places explicit waters and metal ions on protein structures with sub-Å accuracy.
• Explore how these predictions inform structure-based design.

Explore how AI-driven digital twins and functional models integrate patient-specific biology to identify and validate high-confidence drug targets by simulating system-level responses to genetic or pharmacological perturbations.
Learn how perturbation modelling with multiomic and functional genomics data predicts the effects of interventions on disease pathways, while LLMs synthesize data to uncover and prioritize novel therapeutic targets.

Equip teams with AI tools that capture process knowledge and simulate scale-up scenarios, reducing tech transfer timelines and improving first-batch success rates - critical for aligning R&D, MSAT, and manufacturing expectations early.

Gain actionable strategies for embedding generative AI and large language models into early-phase trial design and execution, from protocol drafting and site selection to patient engagement, accelerating timelines while ensuring data quality and compliance

Reinforces trust and compliance by embedding robust data governance frameworks and ethical AI practices, to responsibly harness data and mitigate future regulatory risks.

Explore how AI-driven approaches enhance high-throughput screening by optimizing DNA-encoded libraries (DEL) for rapid identification of potential drug candidates.
Learn how AI algorithms accelerate the analysis of complex screening data, enabling more efficient lead discovery and targeting of molecular interactions.