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

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

Quine connects knowledge across biology, reason about experiments, and participates in scientific progress.

Quine is an AI research system designed to accelerate biological discovery by connecting models, tools, literature, and researchers. It has already been used to prioritize therapeutic compounds for tumor-state shifts, with top candidates validated in wet-lab assays. This represents a significant step towards faster, more effective drug discovery and medical advancement.

SOURCE     Microsoft Research (tier 1)
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DOCUMENT   ce6c2ca3-6c69-41e2-9f11-b3607c8ccbb3
           https://microsoft.com/en-us/research/blog/introducing-quine-an-ai-research-system-designed-for-the-complexity-of-biology
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EVIDENCE   2 extracted excerpts
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MODEL RUN  3 runs, methodology 1.0.1
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SCORE      +2  (Mixed or uncertain)
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CONFIDENCE 75%