Incyte (Nasdaq:INCY) and Edison Scientific announced a strategic collaboration to employ Kosmos, Edison’s AI scientist, for Incyte’s discovery and development work.

Kosmos will be embedded across the Incyte discovery and development lifecycle, enabling continuous learning from translational and clinical data, real-time synthesis of evidence and predictive models of therapeutic performance.

The initial deployment will be focused on high-impact use cases in target discovery and validation and translational biology, centered on embedding Edison’s AI capabilities within Incyte’s research workflows to support more efficient exploration of experimental, clinical and biomarker data with the potential to expand across Incyte’s broader R&D organization. The companies will work together to measure impact on decision quality and long-term pipeline productivity as the system evolves.

“Our vision is for our data to become a learning system that enhances every decision,” said Pablo J. Cagnoni, M.D., President and Global Head of Research and Development at Incyte. “This partnership aims to maximize our data’s value by integrating AI to guide experimental design and improve the quality and consistency of scientific and development decisions. Our goal is not just faster development, but better outcomes across our programs.”

“By using systems that learn from our experimental and clinical data, we can enhance result interpretation, creating a feedback loop that boosts both speed and quality in future programs,” added Patrick Mayes, Ph.D., Executive Vice President and Chief Scientific Officer at Incyte.

At the core of the collaboration is a new model for biopharma: one in which a company’s data is not just stored and analyzed, but becomes a compounding asset, used to train AI systems that improve over time and systematically enhance experimental and clinical outcomes.

“Most AI efforts in pharma treat data as something to analyze,” said Sam Rodriques, Ph.D., Chief Executive Officer of Edison Scientific. “What we are building treats data as something to learn from continuously. The result is a system that compounds—where every experiment, every clinical readout and every decision improves the underlying models. That is how companies, like Incyte, will turn their data into a sustainable advantage over their competitors.”