Introducing Quine: An AI research system designed for the complexity of biology
Introducing Quine: An AI research system designed for the complexity of biology Published September 29, 2026 By Nicolo Fusi , VP and Distinguished Scientist Jonathan M. 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.
- ▪Introducing Quine: An AI research system designed for the complexity of biology Published September 29, 2026 By Nicolo Fusi , VP and Distinguished Scientist Jonathan M.
- ▪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-
- ▪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.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Microsoft Research |
| Canonical URL | https://www.microsoft.com/en-us/research/blog/introducing-quine-an-ai-research-system-designed-for-the-complexity-of-biology/ |
| Publication time | Tue, 29 Sep 2026 14:00:02 +0000 |
| Retrieval time | 2026-09-29T14:06:22.612Z |
| Last seen | 2026-09-29T14:06:22.612Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | d55j8tky6LHw · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Introducing Quine: An AI research system designed for the complexity of biology Published September 29, 2026 By Nicolo Fusi , VP and Distinguished Scientist Jonathan M. Carlson , Vice President Share this page Share on Facebook Share on X Share on LinkedIn Share on Reddit Subscribe to our RSS feed 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Microsoft Research.
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