Introducing GPT-Rosalind for life sciences research

Introducing GPT‑Rosalind for life sciences research A new purpose-built model to accelerate scientific research and drug discovery. Today, we’re introducing GPT‑Rosalind, our frontier reasoning model built to support research across biology, drug discovery, and translational medicine. The life sciences model series is optimized for scientific workflows, combining improved tool use with deeper understanding across chemistry, protein engineering, and genomics. On average, it takes roughly 10 to 15 years to go from target discovery to regulatory approval for a new drug in the United States. Gains made at the earliest stages of discovery compound downstream in better target selection, stronger biological hypotheses and higher-quality experiments. Progress in the life sciences is constrained not only by the difficulty of the underlying science, but by the complexity of the research workflows themselves. Scientists must work across large volumes of literature, specialized databases, experimental data, and evolving hypotheses in order to generate and evaluate new ideas. These workflows are often time-intensive, fragmented, and difficult to scale. 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. By supporting evidence synthesis, hypothesis generation, experimental planning, and other multi-step research tasks, this model is designed to help researchers accelerate the early stages of discovery. Over time, these systems could help life sciences organizations discover breakthroughs that wouldn’t otherwise be possible, with a much higher rate of success. GPT‑Rosalind is now available as a research preview in ChatGPT, Codex, and the API for qualified customers through our trusted access program. We’re also introducing a freely accessible Life Sciences research plugin for Codex, helping scientists connect models to over 50 scientific tools and data sources. We are working with customers like Amgen, Moderna, the Allen Institute, Thermo Fisher Scientific, and others to apply GPT‑Rosalind across workflows that accelerate research and discovery. The model is named after Rosalind Franklin, whose rigorous research helped reveal the structure of DNA and laid foundations for modern molecular biology. The GPT‑Rosalind life sciences model series is built for modern scientific work across published evidence, data, tools, and experiments. In our evaluations, it delivers the best performance on tasks that require reasoning over molecules, proteins, genes, pathways, and disease-relevant biology, and it is more effective at using scientific tools and databases in multi-step workflows such as literature review, sequence-to-function interpretation, experimental planning, and data analysis. This is the first release in our GPT‑Rosalind life sciences model series, and we will continue to expand the frontiers of the model’s biochemical reasoning capabilities across long-horizon, tool-heavy scientific workflows. OpenAI’s compute infrastructure gives us the ability to continue training, evaluating, and improving increasingly capable domain models against real scientific tasks—helping these systems become more useful as the workflows themselves become more complex. We are working with leading pharmaceutical, biotechnology, and research customers, as well as life sciences technology organizations, to apply GPT‑Rosalind across workflows that drive discovery.…