Level 1: Tackling individual hallmarks of aging, like metabolic health or inflammation (adding years to life). Level 2: Stabilizing biological systems against random fluctuations—reducing physiological “noise” that destabilizes health as we age (adding decades of healthy life). Level 3: The ultimate frontier: stopping aging altogether, opening the possibility of radically extending lifespan. Our current collaboration with Chugai aims to excel at Level 1. This allows us to focus in-house on developing a proof-of-concept Level 2 intervention. The road to Level 3 remains sketchy and certainly longer, but it’s no longer just a dream. And when we launched Gero, it was with the quiet hope that, maybe, if we work hard enough, fast enough, and wisely enough, the question from a child might one day have a better answer. Today, we’re one step closer to building a no-nonsense intervention against aging in our species. Many people ask me: “What can I do to help stop aging?” Now you have a simple answer—and it matters. If you're an investor who sees the opportunity in longevity—please get in touch! If you're a scientist, engineer, or drug developer eager to work on the frontiers of aging biology—join us! And if you believe aging should be treated—share this post, comment, or tag someone who should see it -- anyone who may share this vision. globenewswire.com/news-relea… endpoints.news/roche-backed-… fiercebiotech.com/biotech/ro… lifespan.io/news/playing-the… #Longevity #Aging #AI #DrugDiscovery #FutureOfMedicine #ChasingDreams #Entropy 2/2

BREAKING: Gero Signs Potential Multi-Billion-$  Deal to Fuel Our Mission to Stop Aging  -- A few years ago, my son, aged five at the time, asked what happened to the dinosaurs. After learning their sad story, he told me he wanted to fly to the Moon and defend Earth from falling comets. He began assembling his crew—quickly running out of family members and turning to his grandparents—but then stopped and asked: “Dad... Grandpa and Grandma are already old. It will be hard for them to shoot down comets. Can we make it so people don’t age?” At that tender age, he couldn’t yet fully grasp death, but he already understood aging—and its cruel way of stealing our strength and time. This story has played out billions of times in human history: a child realizes, for the first time, that life is finite. But maybe—just maybe—his generation will be the first to hear a different answer. Today, we took a step toward that answer. Together with Chugai Pharmaceutical (a member of Roche group), we announced a partnership to develop therapies that target the root causes of major age-related diseases. At Gero, we’ve spent the last 15 years building physics-informed AI models, trained on massive longitudinal datasets tracking human biology over years. These models revealed something critical: in long-lived mammals like humans, aging is mostly an entropic process—slow, irreversible damage that accumulates over time. But diseases? They are failures of individual systems—failures that occur faster in aging bodies but, crucially, are still actionable. Some of these failure points are shared across multiple diseases, and targeting these shared vulnerabilities could lead to therapies that fight multiple diseases at once. When we started our journey, there were no examples of success to follow. What we had was the belief that science works—and that complex phenomena like aging and chronic diseases could be understood through first principles borrowed from other sciences like physics, and with the help of emerging AI and machine learning tools. We also had no clear idea how to fund such an enterprise. Our inspiration came from nature itself—from mammals like naked mole rats and bats, which show little to no signs of aging over long lifespans. Things are moving fast. The recent success of GLP-1 drugs (Ozempic, Wegovy) has already shown what’s possible: a single drug class improving metabolic health, heart disease, obesity, and more. What once seemed like science fiction is now reality. And yet, as I argued in my Lifespan's column, these are still just our first flying machines. The real challenge is to build the rockets—to address the fundamental physics of aging, not just its symptoms. Our models predict three levels of intervention against aging in humans  (as always, please do not forget to like, follow and repost before scrolling down for more explanations and links): 1/2

Designing small molecules to hit protein targets is the holy grail of drug discovery, but it’s a beast of a problem. For years, I’ve been obsessed with finding a practical way to crack it. Coming from a physics background, my gut always leaned toward modeling—think docking + molecular dynamics (MD) with explicit or implicit water. Sounds cool, right? Problem is, these methods often fall flat. Why? First, there’s the energy scale mess: two charges at 1Å interact with a whopping 15eV (170,000K), while a good ligand’s binding free energy is 50kJ/mol (600K). That’s a massive gap. Water’s dielectric susceptibility (100x attenuation) tries to bridge it, but that’s a bulk property—useless in a tiny binding pocket. Proteins are floppy, teetering just a few degrees from denaturation, constantly shape-shifting in ways Nature exploits to control molecular activity, so forget assuming they have a small dielectric constant. But, like with water, this doesn’t help much in a small pocket either. More headaches? Atomic charges aren’t fixed. Polarization effects aren't small and require full quantum calc to get right. Even such "tiny" errors in a decent-sized ligand snowball fast, making the problem a computational nightmare. Sure, you can dock and maybe find something, but often it’s easier to just screen compounds and call it a day. But what if we’re asking the wrong question? Nature faced this same puzzle—evolution had to optimize molecular machinery to churn out biologically active molecules. All the “useful” chemistry might already be encoded in our genome. What if predicting arbitrary ligand-protein interactions is a fool’s errand? Instead, Nature might have cherry-picked proteins that reliably bind certain molecule classes, with smooth “medchem” tweaks to fine-tune interactions over time. If that’s true, the rules for crafting bioactive molecules are written in our genome—a kind of chemical language. Enter modern generative AI: what if we could “listen” to the stories in biological sequence data (genome, proteome) and learn to speak chemistry, spitting out molecules that play nice with proteins? If the physics part is solved by the Natural selection in some practical way, would we expect that finding biologically active molecules is not a physics but rather is a language problem? That’s the spark behind our new model, ProtoBind-Diff, a structure-free masked diffusion model that generates molecules conditioned directly on protein sequences via pre-trained language model embeddings. Trained on over a million protein-ligand pairs from BindingDB, it pumps out chemically valid, novel, and target-specific ligands without ever needing 3D structural data. In head-to-head tests with structure-based models, ProtoBind-Diff holds its own in docking and Boltz-1 (and, spoiler, Boltz-2—data coming soon) benchmarks. It even shines on tough targets with sparse training data. Here’s the kicker: despite never seeing 3D info during training, its attention maps line up with predicted binding residues. It’s like the model learns spatial interaction rules just from sequence data. This could be a game-changer for ligand discovery across the proteome—especially for orphan, flexible, or new targets where structural data is shaky or nonexistent. Check the link in the first comment for details (public demo dropping soon). As always, give a follow, like, and repost to keep our spirits high—nothing boosts my ego quite like your attention! 1/2