GPT-Rosalind in Codex targets life sciences discovery workflows

OpenAI's GPT-Rosalind is a Life Sciences model embedded in Codex, designed to move scientists from raw scientific inputs to evidence-backed hypotheses and research decisions across discovery workflows. In the demonstrated use case, the model is tasked with comparing and prioritizing three asthma targets — IL-33, TSLP, and IL-1 RA1 — starting from an internal evidence package that includes internal assay results, biomarker strategy, tractability and safety assessments, and a target product profile. The model produces a top-line ranked recommendation grounded in local data files, while also flagging opportunities to expand the evidence base with human genetics or target disease data.

One sub-agent, named Pascal, is specifically directed to handle all human genetics evidence relevant to the three targets, outlining the relevant skills needed to retrieve the appropriate genetic data.

To deepen the analysis, Codex spawns six sub-agents, each assigned to a separate lane of evidence — including human genetics, translational biology, and regulatory context — keeping those streams independent and unbiased until a final synthesis step. One sub-agent, named Pascal, is specifically directed to handle all human genetics evidence relevant to the three targets, outlining the relevant skills needed to retrieve the appropriate genetic data. Once all six agents complete their outputs, the model synthesizes results by surfacing locus-to-gene context, following signals across cohorts, and integrating target disease evidence and literature to resolve ambiguity. The model is described as being primed with "greater thinking and bio-intelligence" for complex scientific tasks.

Key facts

1. 01GPT-Rosalind is a Life Sciences model from OpenAI integrated into Codex for drug discovery workflows.
2. 02The demo compares and prioritizes three asthma targets: IL-33, TSLP, and IL-1 RA1.
3. 03The model starts from an internal evidence package covering assay results, biomarker strategy, tractability, safety, and a target product profile.
4. 04Codex spawns six sub-agents to handle separate evidence lanes — including human genetics, translational biology, and regulatory context — independently before final synthesis.
5. 05A sub-agent named Pascal is assigned responsibility for all human genetics evidence.
6. 06The model can invoke a Life Sciences research plugin to pull in additional external evidence.
7. 07The model surfaces locus-to-gene context, cross-cohort signals, target disease evidence, and literature to resolve ambiguity.