Introducing Quine: An AI research system designed for the complexity of biology

Introducing Quine: An AI research system designed for the complexity of biology - Microsoft Research

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At a glance

Quine (opens in new tab) is a research effort to create a multimodal world model of biology and an interactive harness connecting models, scientific tools, literature, and researchers.

In collaboration with researchers at the Broad Institute of Harvard and MIT, we have used this system to prioritize compounds predicted to drive therapeutic tumor-state shifts and validated several top-ranked candidates across multiple wet-lab assays.

The Quine Fellows program (opens in new tab) will give a cohort of scientists access to the system and an opportunity to accelerate their own research and provide scientific feedback.

Quine is experimental research technology intended only for research, not clinical or medical use, and its outputs may be incomplete or inaccurate and require review by qualified researchers and appropriate scientific and experimental validation. As the technology matures, we expect to expand access through products like Microsoft Discovery (opens in new tab) .

For more than two decades, Microsoft Research has worked at the intersection of computation and biology. Our research has spanned immunology, virology, genomics, biomedical imaging, cell biology, and protein engineering. That work has produced foundational methods, new science, and technology that reached the clinic, from rare and infectious disease diagnosis to cancer biomarker detection.

Across that work, one lesson has become increasingly clear: biology does not divide itself into the neat boundaries our models and tools often do. Genes influence proteins; proteins interact within cells; cells organize into tissues; and experiments continually reshape what scientists know and what they choose to ask next. Making progress on the hardest biological questions therefore requires more than increasingly capable models of individual datasets or tasks. It requires systems that can connect knowledge across scale and modalities, reason about experiments and evidence, and participate in the iterative process through which science advances.

Today, Microsoft Research is introducing Quine (opens in new tab) , a research effort designed to work across those boundaries, reflecting our long-term vision for a discovery system that evolves through scientific use. Quine brings together a world model of biology with a harness that connects scientific tools, literature, the wet lab, and the researchers using them.

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The limits of experimentation

Even as experimental techniques have improved and wet-lab throughput has increased, biology remains fundamentally constrained by time and complexity. Nature cannot be rushed, nor can it be derived from first principles. Experiments are slow, iteration cycles are long, and many of the most important questions involve interactions, combinatorial design spaces, and downstream effects that are simply too large to explore experimentally alone.

At the same time, advances in large-scale machine learning, particularly the emergence of general-purpose foundation models and reasoning models that can iteratively work through problems, suggest a new possibility. These systems are beginning to demonstrate capabilities beyond pattern recognition: integrating information across domains, reasoning over abstractions, and supporting iterative problem-solving. Just as importantly, many of the techniques developed for human language have proven remarkably adaptable to aspects of biology, enabling models to learn representations of biological systems across diverse data types and scales.

This raised a provocative question for us: what would it take to build a world model for biology? By world model , we mean a system that can represent the state of a biological system, predict how that state will evolve in response to interventions, and reason over the consequences of those interventions multiple steps into the future. Such a system would not replace experimentation. Rather, it would allow us to use computation to explore, propose, rank, and prioritize potential paths forward before committing scarce laboratory resources. Our north star is a future in which scientists, models, and experiments operate in a continuously accelerating loop.

What we’ve built

Quine is our first step toward that vision. It includes both a world model of biology and a harness that connects the model to orchestration and reasoning models, scientific tools, the literature, and the teams of scientists using them.

Figure 1: Quine’s two frontier components — a world model of biology and an interactive harness — sit inside a loop that begins and ends with the scientist. A question becomes a set of proposals, proposals become designs worth testing, and the experiment returns measurements that sharpen both the scientist’s...