1. Regulatory workflows are complex but structured.

The presentation highlights that regulatory processes—spanning data management, authoring, reviewing, publishing, and health authority queries—are intricate yet follow consistent patterns. They are highly collaborative, interdependent, and mission-critical to bringing therapies from candidate nomination to market

2. AI is powerful but needs context and precision.

While AI excels at understanding and summarizing information, it struggles with reasoning and lacks domain-specific (drug development) context. Effective use of AI in regulatory work requires clear task definition—large enough to matter, but small enough to manage

3. Human-AI collaboration transforms regulatory efficiency.

When applied thoughtfully, AI can make regulatory work up to 100× faster without compromising quality—reducing months of effort to hours. Studies with Takeda and partnerships with Parexel demonstrate how AI can accelerate timelines, elevate human expertise, and make portfolio knowledge computable across programs

Learn how AI models enhance physics-based simulations to predict molecular interactions and optimize drug design.
Discover the synergy between machine learning and classical methods to accelerate screening and improve the accuracy of drug discovery.

Explore how AI enhances biomarker discovery by analyzing large datasets to uncover novel biomarkers for disease diagnosis and therapeutic efficacy.
Learn how integrating digital biomarkers with AI improves the interpretation of data from wearable devices and traditional lab-based biomarkers for better patient stratification and treatment personalization.

Examine how AI models are being developed, validated, and governed to meet regulatory expectations, with practical insights into documentation, auditability, and lifecycle management to ensure safe, transparent, and compliant deployment in GxP environments.

Guides strategic IT decisions by clarifying trade-offs between cloud and on-premise solutions, to align infrastructure strategy with agility, security, and compliance objectives.

Explore practical strategies for scaling AI implementation across clinical development pipelines, enabling faster trial execution, smarter protocol design, and improved patient recruitment while aligning with evolving regulatory expectations.

• Explore how AI models predict protein 3D structures from sequences, enabling insights into folding pathways and functional conformations
• Examine foundational models that reveal protein–protein interactions and guide design of innovative drug candidates 

• Learn how AI-driven approaches integrate multiomics data, including genomics, proteomics, and transcriptomics, to identify potential drug targets and disease biomarkers for complex diseases.
• Explore how AI models synthesize cross-omic data and real-time multiomic information to uncover novel biological mechanisms, identify potential biomarkers and enable precision medicine.

Demonstrate how AI-driven initiatives - like predictive modelling and automated inspection -translate into measurable outcomes (e.g., defect reduction, shorter batch release cycles) that justify capital investment and cross-functional prioritization.