Bolsters innovation agility by embedding ML Ops practices, aligning data science and IT workflows to ensure reliable, scalable AI deployments and a culture of continuous improvement.

Discover how ML and active learning techniques are revolutionizing the search for promising drug candidates in vast chemical libraries, accelerating hit identification.
Learn how AI models navigate ultra-large chemical spaces, prioritize bioactive compounds, and streamline the discovery of potential hits for further development.

Explore how AI accelerates antibody discovery by enabling de novo design, epitope prediction, and in silico affinity maturation for highly specific, developable therapeutics.
Learn how deep learning and structure-based models optimize antibody stability, immunogenicity and target binding to advance precision biologics.

Explore how ML-enabled real-time control systems and continuous process verification improve yield predictability, reduce rework, and enable faster release - offering a direct line of sight to cost savings and product quality gains.

Strategic insights from complex, high-dimensional healthcare data, fostering integrated analytics and strengthening the organization’s competitive edge in precision medicine.

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.