GPT-Rosalind: OpenAI's AI Revolutionizing Biomedical Research

Introduction of GPT-Rosalind for Life Sciences Research

OpenAI has recently introduced GPT-Rosalind, an advanced artificial intelligence model specifically designed to support research in biology, drug discovery, and translational medicine. This series of life sciences models is optimized for scientific workflows, combining enhanced tool usage with a deep understanding of chemistry, protein engineering, and genomics.

The development of a new drug, from target discovery to regulatory approval, typically takes between 10 and 15 years in the United States. Advances made during the early stages of discovery lead to better target selection, stronger biological hypotheses, and higher quality experiments. Progress in life sciences is hindered not only by the complexity of the underlying science but also by the complexity of the research workflows themselves. Scientists must navigate vast amounts of literature, specialized databases, experimental data, and evolving hypotheses to generate and evaluate new ideas. These workflows are often time-consuming, fragmented, and difficult to scale.

Improving Researcher Efficiency

OpenAI believes that advanced AI systems can help researchers navigate these workflows more quickly, not only by making existing work more efficient but also by helping scientists explore more possibilities, uncover connections that might otherwise be missed, and arrive at better hypotheses more rapidly. 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 assist life sciences organizations in uncovering breakthroughs that would otherwise not be possible, with a much higher success rate.

Availability and Access

GPT-Rosalind is now available as a research preview in ChatGPT, Codex, and the API for qualified clients through our trusted access program. We are also introducing a free life sciences research plugin for Codex, helping scientists connect models to over 50 scientific tools and data sources. We are working with clients such as 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 contributed to revealing the structure of DNA and laid the groundwork for modern molecular biology.

Designed for Scientific Workflows

The GPT-Rosalind life sciences model series is designed for modern scientific work across published evidence, data, tools, and experiments. In our evaluations, it delivers top performance on tasks requiring reasoning about molecules, proteins, genes, biological pathways, and disease-relevant biology. It is also more efficient in utilizing scientific tools and databases in multi-step workflows such as literature review, sequence-function interpretation, experimental planning, and data analysis.

This is the first version of our GPT-Rosalind life sciences model series, and we will continue to push the boundaries of the model's biochemical reasoning capabilities through complex, tool-rich scientific workflows. OpenAI's computing infrastructure allows us to continue training, evaluating, and improving increasingly capable domain models in the face of real scientific tasks, helping these systems become more useful as the workflows themselves become more complex.

Clients and Ecosystem

We are collaborating with leaders in the pharmaceutical industry, biotechnology, research clients, and technology organizations in life sciences to apply the life sciences model across workflows that lead to discovery, from biological reasoning to evidence synthesis, experimental planning, and translational research.

"The life sciences field demands precision at every step. The questions are highly complex, the data is very unique, and the stakes are incredibly high. Our unique collaboration with OpenAI allows 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." — Sean Bruich, Senior Vice President of Artificial Intelligence and Data, Amgen

Performance and Evaluation

We have evaluated GPT-Rosalind across a range of fundamental capabilities in scientific discovery and industrial research. These evaluations measure core reasoning across scientific subdomains, including chemical reaction mechanisms, protein structure, mutation effects and interactions, as well as phylogenetic interpretation of DNA sequences. They also assess whether the models can support real research workflows by interpreting experimental results, identifying relevant patterns for experts, and synthesizing external information to design follow-up experiments. Finally, they test whether the models can select and utilize the right computational tools, databases, and domain-specific capabilities to enhance their reasoning. Together, these evaluations demonstrate progress in the end-to-end scientific research process and suggest an enhanced ability to help researchers overcome challenging discovery tasks.

Connecting to Tools Used by Scientists

Scientists can use our new life sciences research plugin for Codex, available today on GitHub. This package includes a wide range of modular skills for most common research workflows, designed to help users work across human genetics, functional genomics, protein structure, biochemistry, clinical evidence, and public study discovery.

These skills act as an orchestration layer that helps scientists tackle broad, ambiguous, and multi-step questions more efficiently. They provide access to over 50 public multi-omic databases, literature sources, and biological tools, and offer a flexible starting point for common repeatable workflows such as protein structure search, sequence search, literature review, and public dataset discovery.

Eligible Enterprise users can leverage this plugin in research workflows with GPT-Rosalind for deeper biological reasoning, while all users can utilize the plugin package with our core models.

Trusted Access

We aim to make these capabilities available to the scientists and research organizations best positioned to advance human health while maintaining strong safeguards against biological misuse. The life sciences model is being launched through a trusted access deployment framework for qualified Enterprise clients in the United States, with controls around eligibility, access management, and organizational governance. At the same time, we are making a set of connectors and the life sciences research plugin more widely available so that researchers can use our core models more effectively for life sciences research tasks.

The life sciences model has been developed with enhanced enterprise-level security controls and improved access management, allowing for professional scientific use in regulated research environments. We assess access based on three core principles: beneficial use, strong governance and security oversight, and controlled access with enterprise-level security measures. In practice, this means that participating organizations must conduct legitimate scientific research with a clear public benefit; maintain appropriate governance, compliance, and abuse prevention controls; and restrict access to approved users in secure and well-managed environments. Organizations must also agree to the life sciences research preview terms and comply with OpenAI's usage policies, and we may request additional information as part of the onboarding or ongoing participation.

Getting Started

Organizations can request access through our qualification and security review process. During the research preview, using this model will not consume existing credits or tokens, subject to safeguards against misuse. We will share more details on pricing and availability as the program evolves.