Incyte to Deploy Edison Scientific’s AI Scientist Kosmos for R&D

Editor’s Take: AI continues to redefine how biopharma companies are choosing to best optimize their workflows.

Incyte is entering a strategic collaboration to deploy Edison Scientific’s AI scientist, Kosmos, across its discovery and development lifecycle.

The embedding of the AI tool enables continuous learning from translational and clinical data, along with real-time evidence synthesis. Additionally, the AI scientist will generate predictive models of therapeutic performance.

How Kosmos Leverages AI

Initially, Kosmos’ deployment will focus on high-impact use cases in target discovery and validation, as well as translational biology. By embedding those AI capabilities into Incyte’s research workflows, more efficient exploration of experimental, clinical, and biomarker data should result. This will then have the potential to expand across Incyte’s broader R&D organization.

Incyte and Edison say they will collaborate to measure Kosmos’ impacts on decision quality and long-term pipeline productivity. In doing so, they hope to create a new model for the biopharmaceutical industry.

“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.”

Patrick Mayes, PhD, Executive Vice President and Chief Scientific Officer, Incyte

In this model, the companies say, data will not only be stored and analyzed, but also become a “compounding asset.” The data will train AI systems to improve over future periods of time, systematically enhancing experimental and clinical outcomes.

Incyte and Edison Heads React

Pablo Cagnoni, Incyte President and Global Head of R&D, said the vision is for data to enhance every decision.

“This partnership aims to maximize our data’s value by integrating AI to guide experimental design,” Cagnoni said. He added that this will improve decision quality and consistency.

“Our goal is not just faster development, but better outcomes across our programs,” Cagnoni said.

“Most AI efforts in pharma treat data as something to analyze,” said Edison CEO Sam Rodriques, PhD. “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.”