OpenAI Launches AI Model GPT-Rosalind to Advance Biotech & Pharma Research

The model is designed to reduce the time between target discovery and drug approval

OpenAI has unveiled a new reasoning AI model, GPT-Rosalind, aimed at supporting research in biology, drug discovery, and translational medicine.

The new AI model is named after English researcher Rosalind Franklin and is intended to accelerate drug discovery and streamline research workflows.

The model is designed to reduce the time between target discovery and drug approval, a process that typically takes 10-15 years.

By leveraging GPT-Rosalind, the company seeks to explore more possibilities, surface connections that might otherwise be missed, and arrive at better hypotheses sooner.

In addition, the model is tailored to assist researchers in raising queries on databases, reading the latest scientific papers, using other scientific tools, and suggesting potential experiment pathways.

"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," the company stated.

The model was built on ‌top ⁠of OpenAI's newest internal models.

It is currently available as a research preview in ChatGPT, Codex, and the API for qualified customers through OpenAI's trusted access deployment structure.

As per reports, the company is also planning to launch a ​free Life ⁠Sciences research plugin for Codex, connecting scientists to over 50 scientific tools and data sources.

Further, as part of the rollout strategy, the company is also working with companies like Amgen, Moderna, the Allen Institute, Thermo Fisher Scientific, and others to deploy GPT‑Rosalind across workflows that accelerate research and discovery.

The new development follows the recently announced partnership between OpenAI and Novo Nordisk aimed at advancing drug discovery by applying AI in analyzing complex datasets, identifying promising drug candidates, and shortening overall R&D timelines.

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