Schrödinger has signed a strategic collaboration and software agreement with Bristol Myers Squibb to deploy Bunsen, its agentic AI co-scientist, within BMS’s research organization. The agreement builds on a long-standing partnership between the companies and expands use of Schrödinger’s computational platform to help BMS scientists explore more scientific possibilities, prioritize molecules with greater confidence and accelerate discovery decisions.
Under the new deal, Schrödinger will collaborate with BMS scientists to develop new functionality within Bunsen, alongside its computational technologies for large-scale chemical exploration and RetroSynth, its AI-driven synthesis planning platform. Bunsen is designed to execute Schrödinger’s validated physics-based methods by planning and running complex molecular discovery workflows and interpreting results. Schrödinger said the system is optimized for molecular discovery rather than as a general-purpose agent.
Schrödinger said Bunsen is built to help researchers understand scientific objectives, plan computational strategies, execute discovery workflows and interpret results. The company said the platform combines AI with physics-based simulation to help teams evaluate more hypotheses, explore broader chemical space and make higher-confidence decisions before experimental testing. Early access to Bunsen is now available to select customers, with full commercial release expected by the end of 2026.
BMS said the new capability adds to its existing AI toolkit and will help scientists think differently about how physics-based tools can be used to navigate molecular design space. Stephen Johnson, vice president of computational sciences at BMS, said AI has become a key enabler for scientists by scaling creativity and expertise across the research organization.
Schrödinger Chief Scientific Officer Robert Abel said BMS is a long-standing customer and collaborator and that the company is thrilled to see Bunsen deployed at scale. He said the wider adoption of Bunsen and Schrödinger’s computational platform should support a predict-first approach across discovery.
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