Microsoft Research Debuts Quine, a Multimodal World Model of Biology

Summary

AI Models & Platforms Microsoft Research Debuts Quine, a Multimodal World Model of Biology Add Unite.AI to your preferred sources on Google Microsoft Research on September 29, 2026, introduced Quine, a research effort built around a multimodal world model of biology and an interactive harness that links the model to orchestration and reasoning models, scientific tools, the research literature, the wet lab, and the teams of scientists using them. With the announcement, Microsoft opened applications for the first Quine Fellows cohort and reported that, working with researchers at the Broad Institute of MIT and Harvard, it has used the system to rank compounds predicted to produce therapeutic shifts in tumor cell state, with several top-ranked candidates validated across multiple wet-lab assays. The announcement is authored by Nicolo Fusi, VP and distinguished scientist, and Jonathan M. Carlson, vice president. A World Model Built Across Biological Scales Microsoft defines the world model it is pursuing as one that represents the state of a biological system, forecasts how that state would change under an intervention, and reasons through the downstream consequences several steps ahead. The company says such a system would not replace experimentation; instead, it would let researchers weigh and rank candidate directions computationally before committing limited laboratory resources. Quine’s core model is trained to learn representations shared across biological scales and data types, among them sequence, structure, function, cellular state, and imaging. Its training modalities span genomics, proteins, chemistry, RNA and cell state, and bioimaging. Microsoft reports that joint training across these modalities strengthens rather than dilutes performance: evidence from one modality can inform predictions in another, capturing relationships the company says could stay siloed or be lost if separate single-domain specialist models were orchestrated instead. In Microsoft’s description, the system runs as a cycle that starts and ends with the scientist: a question yields proposals, proposals turn into experiments worth running, and the resulting measurements refine both the next question and the model itself. The model need not be perfect, the company says, adding that it will never perfectly capture biology; it needs only to guide experimental design usefully. The Qu…