Mo Elzek

@cgeorgiaw @prasithg cool! thank you!

@moelzek @prasithg This blog is very how-to oriented, but if you feed your agent of choice with this blog, your idea, and an eval metric it should be able to set up a challenge without an issue. huggingface.co/blog/hugging-s…

🤗🤗🤗introducing Hugging Science -- the home of AI for science 🤗🤗🤗 open models and datasets are the powerhouse of science (see the PDB), but finding the models and data you actually need for your breakthrough is hard af you shouldn't need to scrape arxiv, own your own wetlab, fight a custom HDF5 parser, build a fusion stellarator, and beg for compute before you've trained a single epoch so we're changing that we've put all the best science on @huggingface in one place: - 78GB of genomics data - 11TB of PDE simulations - 100M cell profiles - 9T DNA base pairs - 13M molecular trajectories - 400k medical QA pairs and much more, all open, and all ready for training (+ you can also now filter and search by domain, task, and keyword) we've put together all the biggest releases from our partners at NASA, Google, OpenAI, Meta FAIR, Arc Institute, Ginkgo, SandboxAQ, Proxima Fusion, NVIDIA, Ai2, OpenADMET, InstaDeep, Future House, Polymathic AI, LeMaterial, Earth Species Project, Merck, and Eve Bio if you're not sure where you fit in -- work on open challenges for problems that matter: including fusion stellarator design, ADMET, antibody developability, multilingual medicine, catalysis and materials, and scientific reasoning. we're already changing how science gets done: a fusion startup needed a benchmark for stellarator plasma confinement that didn't exist. @proximafusion shipped ConStellaration on Hugging Science: a leaderboard, dataset, and eval metrics, all in one place. a drug discovery team wanted to predict hPXR induction. OpenADMET put up a blind challenge: 11,000+ compounds assayed at Octant, 513 held out, two tracks (pEC50 + structure). Anyone in the world can train and submit. an antibody team at @Ginkgo released GDPa1, a developability dataset for stability, manufacturability, and immunogenicity prediction, with a live leaderboard scoring every submission. if you know a problem the ML community should be working on, let us know. make a challenge! this is about putting all the tools for solving science in one place. so we can hillclimb! → huggingscience.co

@cgeorgiaw @prasithg I have the same question. Can you point me to the blogpost please?

Yeah, 100%! We have a blog on the site about how to set up a challenge, but it’s pretty straightforward. You basically need a frontend leaderboard and an eval metric(s) that you can calculate from what people submit. You can also clone the set-ups of previous challenges that we’ve done. Once you’ve got it, reach out!

It is still early in longevity. 700 orgs only

No more Phase 1, 2, or 3 in clinical trials? The FDA is proposing using AI to get trial data in real-time from EHRs and giving trial design feedback based on what it sees. No more batch processing could eliminate the wait between phases and get therapies to market faster.

Two reports covered in the UK media yesterday point to the same thing. 1. The UK is now sicker for longer. The The Health Foundation reported that healthy life expectancy has dropped from 64 to 61 over the past decade, meaning the last working years of ageing now include more sickness. 2. More Britons will die each year than are born. The Office for National Statistics confirmed that 2026 will be the first year since World War I in which deaths outnumber births in the UK, a trend that will continue. Economies around the world are ageing into deficit. This is the biggest problem of this century. Not fewer resources, but fewer people. Unfortunately, this is not reversible by progressive fertility policy reforms. Children do not enter the workforce overnight; it takes two decades, and they also take their parents out of the workforce. That leaves one option: radical extension of the healthy and productive years of the people already alive. Last week, I had the chance to make the economic case on @LSEnews stage. Longevity is not vanity, and it is not wishful thinking by some billionaires. It is a demographic emergency that needs to have fiscal reforms. For decades, countries have pushed pension age to offset the consequences of ageing populations. But it will not matter much if the last few years before pension are spent sick. Pension age should be tied to the healthy lifespan a country is able to provide, not merely chronological age. Longevity is hyped for the wrong reasons; supplements, clinics, and wearables are a $300 billion industry. In contrast, longevity biotech is the only way to push healthy lifespan, but it is less than $1 billion (10 times less than pet skincare). That is a market failure. Despite this, the science is advancing quickly. Some rejuvenation and senolytic therapies are in clinical trials. Some might argue that GLP-1 drugs are a glimpse of what a “longevity” intervention looks like, with effects on heart, kidneys, liver, brain, ovaries, joints, and addiction, all besides their impact on weight loss and diabetes (their current indications). The numbers make this clear. Scratching the potential of its market (currently ~2%), GLP-1 drugs generated around $70 billion in 2025, compared to $30 billion that AI chatbots like OpenAI and Anthropic made combined last year. It is not a competition with AI. The purpose of technology has always been to create abundance; more, cheaper, better. AI does that for intelligence. But AI will not give you another decade with your parents. It will not pay your country’s pensions. AI is a tool, but the endgame is human life. Thanks to @LSESU_Startups for the invitation and the great organisation.

Daniel is such a great scientist!

Literally the best place to meet people who want to do something about ageing!

1/ How long do you want to live? For most of human history, aging was inevitable. It's now solvable. Apply for LBF8 cohort program: longbiofellowship dot org /THREAD🧵

@MariaRostovsk @The_MRC @GurdonInstitute @Cambridge_Uni Congrats Maria 👏👏

Excited to share some good news - I am starting my own lab at the Gurdon Institute, University of Cambridge, supported by an MRC Career Development Award. Exciting times ahead! 🥳🎉 Recruiting soon - stay tuned! gurdon.cam.ac.uk/people/maria-r… @The_MRC @GurdonInstitute @Cambridge_Uni

@NucleateHQ and @EliLillyandCo are launching a 100k non-dilutive grant for aging. This is a pretty good opportunity if you're early and building around longevity. Repost and tag a friend. Deadline: May 15 Details in the first comment.

Years in the making: a detailed aging gene signature in mice and rats. >30 tissues, high Ns, multi-time points throughout the lifespan. >5000 samples in total. The data is accessible, so you check to see if your favorite gene is age-regulated. science.org/doi/10.1126/sc…

@longevityboris Thanks for sharing this @longevityboris

Great news!!

🚀 The largest-ever open‑source protein‑complex treasure trove - 1.7 million of AI‑predicted complexes now live in the AlphaFold Database In collaboration with @emblebi, @GoogleDeepMind, and @SeoulNatlUni, we have added millions of predicted complexes to the AlphaFold Database to accelerate global health research. 🧵👇

Looking to have deep and honest conversations about drug discovery? Five tables facilitated by pioneers from industry, academia, and startups. Ten seats each. One hard problem per table. Pick your problem and come join 🧵

More scientific data for AI please

Why hasn't AI cured diseases yet? What does it actually take to create the next 100 blockbusters? Learning from pharma & startup successes and failures. 🎙️ From Signal to Drug — Where It Actually Breaks @dives86 — CEO, @ShiftBioscience @jackcastle — CBO, @OchreBio Mythili Iyer — Strategy, @ucb_news Moderated by @LynettaWang126 🗓️ 16 Apr 2026 · 5:30–8:30 PM 📍 London @IDEALondon Co-organised by @LonLongevity & @age1vc. You might leave with a new idea or a meaningful relationship. 🔗 luma.com/fd43mwp2

This might be the beginning of a new business model for frontier AI labs