OpenAI Enters Life Sciences With GPT-Rosalind

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OpenAI has introduced GPT-Rosalind, a reasoning model for life sciences research aimed at biology, drug discovery, and translational medicine. 

The company said the model is designed for scientific workflows, combining stronger reasoning with improved use of tools across fields such as chemistry, protein engineering, and genomics.

Drug discovery remains a long and resource-intensive process. It typically takes 10 to 15 years to move from early target discovery to regulatory approval in the US.

Much of that timeline is shaped by early-stage decisions, such as selecting viable biological targets and designing experiments that can determine downstream success.

OpenAI said these early stages are also where AI could have the most impact.

“We believe advanced AI systems can help researchers move through these workflows faster, not just by making existing work more efficient, but by helping scientists explore more possibilities, surface connections that might otherwise be missed, and arrive at better hypotheses sooner,” it said.

Scientific research workflows are often fragmented, requiring scientists to navigate large volumes of literature, specialized databases, and experimental data while continuously refining hypotheses. 

OpenAI said GPT-Rosalind is designed to assist across these multi-step processes, including evidence synthesis, hypothesis generation, and experimental planning.

The model is being released as a research preview, available through ChatGPT, Codex, and the API for selected users under a controlled access program. 

OpenAI is also launching a Life Sciences research plugin for Codex, which connects the model to more than 50 scientific tools and datasets.

Early collaborators include organizations such as Amgen, Moderna, the Allen Institute, and Thermo Fisher Scientific, which are exploring how the model can be applied to accelerate research workflows.

“The life sciences field demands precision at every step,” said Sean Bruich, Senior Vice-President of Artificial Intelligence and Data at Amgen. “The questions are highly complex, the data are highly unique, and the stakes are incredibly high. Our unique collaboration with OpenAI enables us to apply their most advanced capabilities and tools in new and innovative ways with the potential to accelerate how we deliver medicines to patients.”

The model’s capabilities have been evaluated across core areas of scientific reasoning, including chemical reactions, protein structure and interactions, and DNA sequence analysis. 

OpenAI said the system shows improvements in interpreting experimental outputs, identifying relevant patterns, and designing follow-up experiments, tasks that are central to real-world research.

Another focus area is tool integration. GPT-Rosalind is designed to select and use appropriate scientific databases and computational tools as part of its reasoning process, reflecting how researchers typically work across multiple platforms and data sources. 

Access to the model is being rolled out cautiously. OpenAI said the system is available initially to qualified enterprise users in the US under a “trusted-access” framework, which includes eligibility checks, governance requirements, and safeguards against biological misuse.

Participating organizations must demonstrate legitimate scientific use, maintain oversight controls, and restrict access to approved users.

At the same time, some components, such as connectors and the research plugin, are being made more broadly available to support life sciences work using existing models. 

The model is named after Rosalind Franklin, the scientist whose work was critical to uncovering the structure of DNA, pointing to the system’s intended role in advancing molecular biology and related fields.

OpenAI described GPT-Rosalind as the first in a planned series of life sciences models, with future iterations expected to improve in handling long-horizon and tool-intensive research tasks.

The company is also working with institutions such as Los Alamos National Laboratory to explore applications, including AI-guided protein and catalyst design.

“This is the first release in our Life Sciences model series, and we view it as the beginning of a long-term commitment to building AI that can accelerate scientific discovery,” the company said.