Physician, author, ML enthusiast. Interested in biology of aging. Tweets = own. 📸: George Dunbar

Detroit, MI
Joined August 2013
Vik Dhillon retweeted
OpenAI is downplaying this for PR reasons. If you’re a mathematician, you must feel like a nuclear bomb hit, and you’re at ground zero. Reasoning models are two years-old. In that time they went from incapable of basic arithmetic to solving problems humans couldn’t solve for decades. Math is only the beginning. AI will revolutionize the entirety of the human scientific endeavor. Most of us don’t appreciate what that means. What a time to be alive!
We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results. github.com/openai/math
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Aging has a language. 🧬 We’re teaching AI to read it. On the cover of Cell: compact Longevity-LLMs, jointly trained by Insilico Medicine and @liquidai, demonstrate SOTA performance on LongevityBench. Powered by these models, Longevity Claw nominated 328 candidate gene targets for aging research. Small model. Big questions. Open source. 🧵👇 #insilicoSOTAFM
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Today, we're excited to share early progress on using Gemini to accelerate scientific discovery in the real-world. We present an extension of Co-Scientist which we use to collaborate with scientists across materials science, biology, and computer science.
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Vik Dhillon retweeted
We are making continuous improvement on Gemini for Biology!
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Vik Dhillon retweeted
New in @BloodJournal: A comprehensive preclinical study evaluated ziftomenib using leukemia cell lines, primary patient samples, xenografts/PDXs, gene-expression profiling, and structural crystallography. Ziftomenib suppressed MEIS1/HOXA signaling, induced differentiation, and caused leukemia regression in NPM1m/KMT2Ar models. CRISPR-engineered MEN1-mutant models showed retained activity against T349M and G331R—but resistance with M327I/G331D. #AML doi.org/10.1182/blood.202503…
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Vik Dhillon retweeted
1/6 MYC has long been considered “undruggable.” Instead of trying to drug the protein, we went after its RNA. This work is part of our long-standing effort to expand the druggability of genetically defined disease targets at the level of mRNA.
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Our paper, “Prognostic impact of FLT3-ITD microclones in young adults with acute myeloid leukemia treated with intensive chemotherapy”, is now published in @BloodPortfolio 🤩 Many thanks to all co-authors and collaborators! Free access (until Sept. 18): authors.elsevier.com/a/1nWx4…
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🔆🔆🔆Matching T-cell receptors (TCRs) to their cognate peptides presented on MHC molecules (pMHCs) from sequence data alone is one of the most important unsolved problems in computational immunology. I've been presenting this work for over a year and am excited to finally share the preprint and public code. A transformative collaboration for our lab with @quaidmorris (see tweetorial 👇👇with links). Led by first authors @Olga_Lyu, Jonathan Levine & Melissa Pathil, with S. Martis, A. Streltsov, Z. Sethna, Y. Elhanati (@elhanati_yuval), Vinod Balachandran (@TheVinodLab)!!! @MSKCancerCenter
Can we predict which T cells recognize which peptides? A lot of the signal is hiding in AlphaFold3's hallucinations. enFoldX: instead of one predicted structure, we use the whole noisy ensemble to predict TCR:pMHC binding. 💻github.com/jonlevi/enFoldX biorxiv.org/content/10.64898…
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In Georgia I entered a Chick-fil-A drive-thru line of forty cars. I turned off my engine. I prepared to wait the way my ancestors waited out a siege. Six minutes later I was holding chicken. I do not know what happened to the other thirty-nine cars. The line moved like water that had somewhere to be. A young woman walked between the cars taking orders on a small tablet, on foot, in the sun, smiling like the sun was her idea. I said thank you. She said, "My pleasure." I did not think about it then. I should have. At the window I thanked the young man for the food. "My pleasure." I thanked him for the sauce. "My pleasure." I thanked him for thanking me— "My pleasure." I want to be clear about what was happening. This was a duel. In Japan I trained in courtesy for forty years. Bowing angles. Seasonal greetings. The correct depth of apology for seventeen distinct situations. I was a white belt in that drive-thru. I escalated. I used my most formal English. I thanked him on behalf of my entire family line. He said, "My pleasure," and gave me extra sauce. Extra sauce. Unprovoked. A counterattack. I returned the next day to reclaim my honor. It was Sunday. The restaurant was closed. All of them were closed. In the entire country. They close every Sunday, and they have closed every Sunday since the beginning, because the founder made a promise, and the promise did not expire when he did. I stood in the empty parking lot and understood I had lost twice. Once to a teenager with sauce. Once to a man I will never meet, who kept his word so long it became a building. In my country we say a samurai's word is his life. I had never seen a restaurant say it back. I returned Monday. I ordered. And this time I said it first, before she could. "My pleasure." She smiled and said, "It sure is." America, I surrender. It was my pleasure.
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Researchers from the Crispino Lab Team at @StJudeResearch led by Dr. Jeremy Wen and Anitria Cotton identify menin as a key regulator of #megakaryocyte proliferation and fibrosis in #myeloproliferative neoplasms. Learn more here at @Cancer_Cell: 🔖 bit.ly/4wGXEZy
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Vik Dhillon retweeted
Google DeepMind
@GoogleDeepMindJul 15
From proposing hypotheses to designing experiments, AI agents are starting to reshape scientific discovery. But the hardest part is testing these ideas in the real world. Our essay explores the growing validation bottleneck and outlines four priorities for policymakers and funders. → goo.gle/4poACUT
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Vik Dhillon retweeted
We just updated our manuscript "Understanding Language Model Scaling on Protein Fitness Prediction". Where we explained why larger pLMs don’t always perform better on mutation effect prediction. We extended beyond ESM2 to models like ESMC, ESM3, SaProt, and ESM-IF1. #ProteinLM
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Excited to share our study on immune interactions in extramedullary (lung) acute myeloid leukemia (AML) out today at @NatImmunol !! Acute respiratory failure frequently occurs in AML due to leukemia infiltration of the lungs. The question is why and what can we do to suppress it. @VarvaraParask in the lab started this work by mapping AML:stromal and AML:immune cell interactions using scRNA-Seq and Visium/Xenium @10xGenomics spatial tools. nature.com/articles/s41590-0…
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Vik Dhillon retweeted
We’re introducing GeneBench-Pro, a research-level benchmark for a harder kind of AI progress: how well agents can navigate messy biological data, choose the right analysis path, and make judgment calls that real computational research depends on. openai.com/index/introducing…
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scRNAデータのバッチ間の統合など、生命情報科学や公衆衛生学的な解析用ソフトウエアを人間よりうまく生成するAIプラットフォームを構築したという論文。間にLLMgがはまっている。これは読まないと不味そうな気がする。Nature。 nature.com/articles/s41586-0…
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Vik Dhillon retweeted
Ummm…
Biology doesn’t need another chatbot. It needs systems that can reason across cells, drugs, species, tissues, and patients. That’s what we’re building at CellType. Excited to be selected for YC Startup School 2026. See you in SF July 25-26. Reach out if you want to partner or build together!
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Vik Dhillon retweeted
Happy to share our new review in @BloodPortfolio, led by Jiaoyi Chen with Benson Wu! We explore how DNA methylation can help retrace a cancer’s history and reveal how tumours evolve and respond to treatment. sciencedirect.com/science/ar…
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Vik Dhillon retweeted
At Tahoe, one of the reasons we are excited about perturbation prediction is to ultimately predict how patient samples will respond to any of the many drugs in our library
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1/ Another single-cell study drops. 500,000 cells sequenced. More UMAP plots. More clusters. But here’s the question: Are we learning more—or just counting better? 🧵
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