Data, Data in the Well: Who’s the FAIRest of them all? A Tale of In Vitro Characterization of Biologics Preparing for the AI Era
In this webinar, Pieter Kennis, Senior Principal Scientist for Large Molecule Research at Sanofi, shares how the discovery engine for NANOBODY® molecules evolved from fully manual workflows to a cutting‑edge, AI‑powered lab environment. Early screening and characterization campaigns depended heavily on human intervention, low‑throughput analysis, and siloed spreadsheets. Today, the team operates at the intersection of automation, machine learning, and intelligent analytics—an approach that reflects the broader shift toward AI biopharma research and development and the growing importance of AI ML drug discovery.
This transformation is grounded in FAIR data principles, strong AI data integrity, and rigorous AI data governance aligned with ALCOA+ principles. These foundations ensure that data is trustworthy, traceable, and ready for advanced computational methods. As Pieter walks through the phased integration of robotic systems and digital workflows—from semi‑automated processes to fully integrated platforms—he highlights how end‑to‑end automated data capture has reshaped how binding and functional assay data are generated, managed, and interpreted.
The resulting structured, high‑quality datasets now support machine‑learning models for assay optimization, hit triage, and lead‑candidate selection—core capabilities for drug discovery using AI and AI drug discovery. This evolution demonstrates how AI in scientific discovery and AI in scientific research can accelerate decision‑making, reduce manual bottlenecks, and enhance scientific rigor across increasingly complex NANOBODY® formats and biological targets.
Sanofi’s AI‑native approach also illustrates the expanding role of AI in therapeutic innovation, including AI precision medicine and the broader role of artificial intelligence in precision medicine. By leveraging scalable AI cloud solutions, the team can unify experimental, processed, and in silico data, enabling real‑time insights and supporting global collaboration across research sites.
This webinar explores the challenges, breakthroughs, and strategic impact of embracing automation, FAIR data practices, and AI‑driven discovery. Together, these elements form the foundation of the AI‑powered lab of the future—one where intelligent systems, robust Data governance AI, and predictive modeling converge to advance AI in biopharma and unlock the full potential of next‑generation biologics discovery.