Anson Zhou

MD/MBA @stanford | Health + Bio @pearvc

[OpenAI @OpenAI]

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.

The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.

The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

[Eric Topol @EricTopol]

Another major advance vs cancer! @ASCO #ASCO26
Personalized neoantigen mRNA vaccine 5 year follow-up vs metastatic melanoma reduced recurrence and death by 49% (on top of Keytruda)
https://t.co/NadITTYIT2

[Adam Feuerstein ✡️ @adamfeuerstein]

Incredible #ASCO26 moment.

Dr. Brian Wolpin, presenter of the daraxonrasib study, received a standing ovation DURING his talk after he stated the survival benefit for PDAC patients. It was sustained. Cheering. I have never see anything like it in the middle of a talk. $RVMD

[Eric K. Singhi, MD @lungoncdoc]

7yr CROWN update of 1L lorlatinib

55% progression-free at 7 YEARS.
Median PFS STILL not reached.

In metastatic solid tumors, this is essentially unheard of. Let that sink in.

And hopefully, one day, this becomes the standard (or even better) for ALL metastatic cancers.#ASCO26

[Anson Zhou @ansonyzhou]

@drobiy12 Change to ARBs.

[Crémieux @cremieuxrecueil]

Eli Lilly has done it.

They've gone and made what seems to be a powerful, permanent gene therapy for LDL cholesterol.

That means they'll be able to effectively prevent most heart disease with a single infusion!

[dogs so cute that could save the world @dogssaveworld]

Aww so gorgeous 🐶❤️

[ゴールデンブヒ @goldenbuhi2020]

ハルは川の中をとても慎重に歩く大型犬なのです

[WeRateDogs @dog_rates]

This is Daisy. She helped her human make the bed today. Through moral support. 13/10

[euan ashley @euanashley]

New AI paper from us this week. When my student first showed me his initial findings, I really didn’t know what to make of them. I felt that this was an interesting but curious loophole phenomenon that would shortly be closed. I was very wrong.
https://t.co/H3YIyl01FR

[Anish Moonka @anishmoonka]

Every time you get a cancer biopsy, the lab makes a tissue slide that costs about $5. It shows the shape of your cells under a microscope, and every cancer patient already has one on file.

There’s a much fancier version of that test called multiplex immunofluorescence (basically a protein-level map showing which immune cells are near your tumor and what they’re doing). It costs thousands of dollars per sample, takes specialized equipment most hospitals don’t have, and barely scales. But it’s the kind of data oncologists need to figure out whether immunotherapy will actually work for you. Right now, only about 20 to 40% of cancer patients respond to immunotherapy, and one of the biggest reasons is that doctors can’t easily tell whether a tumor is “hot” (immune cells actively fighting it) or “cold” (immune system ignoring it).

Microsoft, Providence Health, and the University of Washington trained an AI to analyze the $5 slide and predict what the expensive test would show across 21 different protein markers. They called it GigaTIME, trained it on 40 million cells in which both the cheap slide and the expensive test coexisted, and then turned it loose on 14,256 real cancer patients across 51 hospitals in 7 US states.

The results landed in Cell, one of the most selective journals in biology. The model generated about 300,000 virtual protein maps covering 24 cancer types and 306 subtypes. It found 1,234 real, verified connections between immune cell behavior, genetic mutations, tumor staging, and patient survival that were previously invisible at this scale. When they tested it against a completely separate database of 10,200 cancer patients, the results matched up almost perfectly (0.88 out of 1.0 agreement).

Nature Methods named spatial proteomics (mapping where specific proteins sit inside your tissue) its Method of the Year in 2024, and specifically cited GigaTIME in a March 2026 update as a model that “democratizes” this kind of analysis. The full model is open-source on Hugging Face. Any cancer research lab with archived biopsy slides, and most of them have thousands, can now run virtual immune profiling without buying a single piece of new equipment.

[Satya Nadella @satyanadella]

We’ve trained a multimodal AI model to turn routine pathology slides into spatial proteomics, with the potential to reduce time and cost while expanding access to cancer care.

[𝔹𝕀𝔾 𝕃𝕀𝔹ℝ𝔸 ✨ @venusianhunty]

Currently sobbing over this video🥹 they been best friends for 50 YEARS

[Puppies & Positivity @Puppieslover]

This is Bailey. Human tried napping solo on the couch. Bailey said nope and draped right over him like the world's best blanket. we don't deserve dogs.

[Harrison G. Zhang @harrison_zhang]

🚀🤖 Introducing the Virtual Biotech: a multi-agent AI research platform for therapeutic discovery & development

This places a virtual CSO and its cross-functional R&D organization of AI scientists at a user’s fingertips.

Preprint: https://t.co/rK3eBpFmab

[Bo Wang @BoWang87]

Welcome to the Lab of the Future! 🧬🤖

Excited to share LUMI-lab, out today in @CellCellPress — a self-driving platform that pairs an AI foundation model with a robotic lab to autonomously discover ionizable lipids (LNPs) for mRNA delivery.

The core problem: Designing lipid nanoparticles (LNPs) is hard. The chemical space of ionizable lipids is vast, experimental cycles are slow, and — critically — historical LNP datasets are far too small to train a predictive model from scratch. Most AI approaches in this space hit a wall immediately: not enough data to learn from.

Our solution: lab-in-the-loop foundation model learning. Instead of training on LNP data alone, LUMI starts as a transformer-based foundation model pretrained across broad chemical space, building rich molecular representations before it ever sees a single LNP experiment. Then it enters a closed loop with a robotic synthesis platform: predict → synthesize → assay → update. Each round of real wet-lab experiments fine-tunes the model, which then proposes smarter candidates for the next round. The lab isn't just validating AI predictions — it's actively teaching the model, continuously.

What happened when we let it run: LUMI-lab autonomously synthesized and screened 1,700+ ionizable lipids in human bronchial epithelial cells. The top candidate — LUMI-6 — features a brominated lipid tail, a structural motif that had been largely overlooked in LNP design. LUMI found it without being told where to look. When formulated into LNPs and delivered intratracheally to mice, LUMI-6 achieved 20.3% gene editing efficiency in lung epithelial cells — a compelling result for one of the hardest-to-reach therapeutic targets, directly relevant to diseases like cystic fibrosis and alpha-1 antitrypsin deficiency.

Why this matters beyond LNPs: This is a proof of concept for a broader thesis — that foundation model pretraining + active learning + robotic experimentation can overcome the data scarcity bottleneck that plagues AI-driven discovery in biology. You don't need a massive domain-specific dataset to start. You need a model that can generalize, a lab that can generate the right data, and a loop that connects them.

Huge congratulations to first authors Yue Xu, @HAOTIANCUI1, and Kuan Pang, and to the entire @BowenLi_Lab team. Grateful to our collaborators at @UHN and @UofTPharmacy, and to Princess Margaret Cancer Centre Research @PMResearch_UHN.

📄 Paper: https://t.co/5tfD3dkSr7