Building ScienceOS, the operating system to accelerate progress in science and math, https://xitter.cf/t.co/BCUFC6QiWv

Joined August 2024
After 10 incredible years at Google, I've left @GoogleDeepMind to join @thematthewosman and @FabioZB_I at Polyphron as a co-founder and explore the next frontier in my research passions. Together, we're training frontier models that autonomously explore, understand, and physically reproduce human tissue biology. It's our grand vision to grow tissues realistic enough to match human response to all drugs, and realistic enough to recover organ function when transplanted into living organisms. Other than the bitter lesson, I think another lesson remains true in the history of ML: the more directly you can optimize your end goal, the better your outcome will be. IMO this is one lesson largely missed in the drug discovery and computational biology space today, and one that Matt and Fabio understood very clearly (and why I joined them on this journey!). At the heart of our company is one giant RL problem: propose a recipe to grow tissue, sample life itself, reward directly against the functionality of the tissue. If this sounds exciting to you as either an AI or Life scientist, feel free to reach out! We're building the coolest lab in NYC :) I'll never forget all of the {pretraining, RL, self-improvement} projects I worked on with my brilliant friends at GDM over the years, but I'm even more excited to share the things we discover next! (p.s. this isn’t the first time i RL’d something based off of similarity, iykyk)
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In just one day, create and characterize novel materials. In a traditional lab, it takes at least 10 weeks to perform the bare minimum of the scientific process. Radical’s self driving lab invents at the speed of AI, enabling exponentially more material discoveries per scientist. Invention runs on Radical.
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What if designing a protein felt less like writing code and more like sculpting something with your hands? A new bioRxiv preprint introduces ProteinSketch, a VR environment where researchers can physically sketch protein backbones in 3D, shape the volume they want a protein to occupy, and let a diffusion model turn those gestures into molecular structures. Put on a headset, use both hands to draw a topology or define a curved 3D envelope, and RFdiffusion continuously refines that spatial idea into candidate proteins. This sounds almost like a futuristic interface demo, but the deeper problem is real: generative protein models are becoming extremely capable, while communicating high-level spatial intent to them remains surprisingly difficult. A sequence prompt is easy to type. “Build me a protein that bends around this molecular surface, enters this gap, and presents a binding interface here” is much harder. ProteinSketch makes geometry itself part of the conversation. The researchers used volumetric constraints to generate unusual anisometric protein shapes and confirmed shape fidelity by cryo-EM. They also used the same framework for functional binder design and to extend existing minibinders toward molecular surfaces that conventional design approaches struggled to reach. What I like about this direction is that it changes the interface to generative biology. We have spent enormous effort teaching models the geometry of proteins. The next challenge may be giving humans a more natural way to express the geometry they actually want. Maybe the future protein-design language will not be text at all. It may be shape, motion and space. Preprint : biorxiv.org/content/10.64898…
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Introducing Discovery Loop ♾. We’re building AI to run experiments at unprecedented scale and to solve the biggest bottlenecks in science and engineering. Our founders @JeffDean, @Sanjay_Ghemawat, @quocleix, @OriolVinyalsML helped build the AI infrastructure and models that scaled the modern world. Now, we are scaling discovery itself. The future can't wait. Learn more at: discoveryloop.com
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BioX retweeted
An incredibly packed room for our morning speakers Nathan Frey @nc_frey from Anthropic and Vivek Natarajan @vivnat from Google DeepMind at the AI Scientist Summer Workshop. We still have lots of fantastic speakers throughout the day! Checkout the schedule at ai-scientist-workshop.github…
We're organizing an AI Scientist Summer Workshop in Boston on August 4! We have already received an incredible amount of interest, so please register soon to secure a spot. We have an incredible line up of speakers from Harvard, MIT, Anthropic, Google DeepMind, AI2, and Microsoft Research. Marinka Zitnik (HMS) @marinkazitnik Vivek Natarajan (Google) @vivnat Nathan Frey (Anthropic) @nc_frey Paul Liang (MIT) @pliang279 Michael Brenner (Harvard, Google) Markus Buehler (MIT) @ProfBuehlerMIT Yuanqi Du (MSR) @YuanqiD Bodhisattwa Majumder (AI2) @mbodhisattwa Rafael Gómez-Bombarelli (MIT, Lila Sciences) @RGBLabMIT We're also welcoming contributed talks and posters. Please fill in the registration form to submit a talk or poster 👇 Thank you to my fellow organizers @WengongJin @YuanqiD
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I had so much fun creating the Self-Improving AI Agents course with @achowdhery, and teaching it twice in one year at Stanford! We also collaborated with Stanford Online to make the course available online: YouTube: youtu.be/6YnLB0XbTnI?si=MVwR… The field is moving incredibly fast, but we tried to focus on the core concepts that help us build better AI systems. I hope you enjoy it!
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OpenAI
@OpenAIJul 29
We’re giving scientists, mathematicians, and engineers free access to our frontier models—starting with 10,000 researchers and expanding to 100,000 through 2027. ChatGPT for Academic Researchers is built to accelerate discovery across disciplines.
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It's really cool to see some of these recent papers from @davidrliu lab and Doudna lab using generative models for protein sequence design (a.k.a. "inverse-folding" AI models) in combination with evolutionary insights and techniques. In the Liu lab paper, nature.com/articles/s41586-0… they used proteinMPNN (my beloved) to improve important properties like thermostability and expression, and enhance enzymatic activities for proteases. Critically, they redesigned regions were not allowed to touch the active site so the mutations are expected to improve the structural stability of the overall fold. They did this to "polish" evolutionary starting points and also to seed laboratory evolution trajectories. Both approaches improved nontrivially on what would have been sampled through evolution alone. In the Doudna lab paper, science.org/doi/10.1126/scie… they used ESM-IF1 to make a synthetic Cas12 variant with improved activity compared to the native. Critically, they avoid mutating the most evolutionarily conserved positions. They suggest the structure structure aware ESM-IF is able to explore much more diverse areas of sequence space than standard sequence-only pLMs. In both of these cases, generative models for protein sequence design added genuine value when combined with expert scientific intuition on which problems to apply them to and how to configure ("prompt") them. These papers coming out is kind of a full circle moment for me; I worked on this older paper, cell.com/iscience/fulltext/S… with Doudna on Cas12a which is the same Cas family they chose to make a synthetic version of, right before I went to @UWproteindesign and got to help with proteinMPNN. Congrats to the authors @NicholasKrasnow et al. and @PetrSkopintsev @isabelesain @evandeturk et al. #ProteinDesign #CRISPR #ArtificialIntelligence
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The list keeps growing (a few notable AI developers are still missing, @elonmusk, @ajassy, @DarioAmodei). AI-policy-wise, @JensenHuang's post was one of the most unifying things I've ever seen on this platform. Many agree that strengthening open-source AI is the way forward. It's probably one of the best ways to improve cyber defense too. The threats are increasing at the same pace as AI development, so it's great to see some level of consensus. The whole AI ecosystem needs to get stronger, and now it's time to work.
OpenAI joined the coalition supporting open-source AI. A few notable AI developers are still missing...
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During today's #ICM2026 opening ceremonies, the IMU announced the 2026 Fields Medals recipients: @UChicago's Yu Deng, @stonybrooku's John Pardon, @UofT's Jacob Tsimerman, and Hong Wang of @nyuniversity and @Institut_IHES. Read more: bit.ly/4psOJbU
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New @ChemRxiv preprint: AIMNet2(Score), a label-free way to score protein-ligand complexes. No experimental affinity data. Just QM-level physics from AIMNet2 MLIPs: interaction energy + ligand desolvation + conformational strain, on a single bound structure. #compchem🧵
🤖 Made with AI
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This is great news. As you age, sugar binding to your proteins creates stiff, sticky chemical scars that affect skin, arteries, eyes and more. It was considered irreversible and now may be reversible, restoring to a healthy state. Researches did this by using AlphaFold to search 45,000 oxidases, then screened more than 500 million engineered variants through directed evolution. The work is still ex vivo in a lab setting. Delivering a large bacterial enzyme safely into living tissues, with sufficient penetration and bioavailability, remains a major challenge.
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We’re expanding our work with the US Dept. of @ENERGY on the Genesis Mission – an initiative to double the pace of scientific discovery within a decade. 🧪 By committing $40M in AI tokens and @GoogleCloud credits, more lab researchers will gain access to Gemini and other AI models. → goo.gle/4yGqKKD
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OpenAI
@OpenAIJul 23
Health in ChatGPT is starting to roll out to U.S. users. You can securely connect Apple Health and supported medical records to understand your information in context, track what has changed, and have more informed conversations. openai.com/index/health-in-c…
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Anthropic
@AnthropicAIJul 20
We're offering grants of up to $50,000 in Claude usage credits to researchers accelerating cures for rare diseases. This is our first focused call within AI for Science, our program supporting scientists using Claude to speed up discovery. anthropic.com/news/rare-dise…
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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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In the last few months, Eli Lilly, Pfizer, and Novartis have all chosen to partner with @chaidiscovery. Great momentum!
We’re delighted to announce our collaboration with @Novartis. Building on a year-long technical engagement, we’re expanding our collaboration with Novartis to support broader deployment of our technology across multiple therapeutic programs. Novartis will gain access to our latest models for prediction and design, including Chai-3.
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Convention wisdom is that bioweapons are humanity's greatest weakness – 100x cheaper to make than to defend against. Andrew Snyder-Beattie thinks conventional wisdom is likely wrong. He has a plan cheap enough to do without government. Useful even in worst case scenarios like mirror bacteria. Effective enough to save most people. In one of my all-time fav interviews he lays out a low-tech 4-step approach developed by his research team at Open Philanthropy, to fix a problem most have thought unsolvable. ASB is hiring for many roles in this project from logistics to biotech to manufacturing, and has $100s millions to deploy. Enjoy, links below! 2:10 How bad it could get 9:19 The worst-case scenario: mirror bacteria 18:14 Why low-tech 25:30 Prevention 31:21 The “4 pillars” plan 33:09 ASB is hiring now to make this happen 35:11 Everyone was wrong: biorisks are defence dominant 40:23 Pillar 1: Lungs 55:53 Pillar 2: Biohardening 1:15:19 Pillar 3: Detection 1:28:40 Pillar 4: The wrench hypothesis 1:40:12 The plan's biggest weaknesses 1:44:44 Would chaos make this impossible to pull off? 1:51:50 Would rogue AI make bioweapons? 1:57:57 We can feed the world even if all the plants die 2:07:03 Could a bioweapon make the Earth uninhabitable? 2:09:35 What ASB is hiring for 2:30:27 How to protect yourself and your family (On the 80,000 Hours Podcast, available anywhere you get podcasts.)
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Google is proud to be a Diamond Sponsor of the 2026 International Conference on Machine Learning (#ICML2026), kicking off on July 7th in Seoul, Korea! Attending @icmlconf? Come by our booth (#B206) to learn more about our work, including papers, workshops, and booth activities at goo.gle/4y1doIs
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New @Nature publication from @GoogleResearch & @GoogleDeepMind: In this study, we advance AMIE, our research medical AI, from one-off diagnostic conversations toward treating & managing disease over time, using clinical guidelines & drug formularies. More: goo.gle/4oA0EEb
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