Why We’re Becoming Verge Labs
The next chapter for Verge, and a new bet on what AI can do for neuroscience
We started Verge Genomics eleven years ago to transform drug discovery with human data. To find drugs that worked in humans, we had to start with humans. For most of the company’s history, we thought the right way to do that was to build the data and AI ourselves, then develop our own drugs.
Today, Verge Genomics is becoming Verge Labs, a frontier AI lab building world models of human disease biology, starting with the brain. The mission is the same, but we’re executing on it in a different way. We are going after what we believe is a fundamentally bigger opportunity, made possible by recent advances in AI. Instead of developing drugs ourselves, we are opening up our platform to other drug developers with the goal of improving clinical success rates across the entire industry.
This is not a decision we came to lightly. It is the product of a decade of building, hard-won lessons from the clinic, and a clear-eyed look at where the field is heading. Here’s how we got here.
What we built and learned from the clinic
Over the last decade, we made a bet: that an AI engine trained on human brain tissue, not mice or cells, could find better starting points for new drugs in complex diseases like ALS and Alzheimer’s disease. We called it CONVERGE. It surfaced over 280 novel drug targets, with an average 83% validation rate in preclinical follow-up. Eli Lilly and Alexion signed partnerships to use it worth $1.6 billion, and in 2024 Lilly nominated two CONVERGE-derived targets into its pipeline. The platform produced two clinical candidates of its own, including one that reached a clinical trial in ALS patients.
Along the way, we built what we believe is the largest proprietary neuroscience patient dataset of its kind in the world: more than 12,000 brain transcriptomes across 6,000 patients, 15 million single-cell profiles, matched proteomic, genomic, and clinical data, and a physical inventory of over 900 frozen brain tissue samples spanning the major neurodegenerative diseases. Last year we added one of the most thoroughly phenotyped clinical and biomarker datasets ever generated in ALS.
That first drug, VRG50635, finished its first ALS patient study in late 2025. The trial did not show clinical benefit and we are not advancing the program. We have written separately about what the trial taught us about the disease, the biology, and gaps in our first-generation platform. Read it here. The short version is that the drug hit its target in the brain and engaged the biology we predicted, but patient heterogeneity in ALS was greater than we had anticipated.
The trial taught us that finding the right target is only half the problem. The other half is knowing which patients to give it to, and how they might respond. Closing that gap is now the focus of our next chapter.
Why one drug at a time did not work
There is a version of this story where Verge keeps developing its own pipeline, takes another shot on goal, and repeats. We considered it carefully and decided against it.
The first reason is scale. The traditional biotech model was not built for a platform like CONVERGE, whose value lies in raising the probability of clinical success across a portfolio. Even a transformational platform that doubled the industry’s success rate would still see more than 80% of clinical programs fail. Proving that requires hundreds of shots on goal, not one or two.
It was also expensive. Developing a drug from target to Phase 2 takes years and tens to hundreds of millions of dollars. When capital is constrained, companies can only advance one drug at a time, and failures happen often for reasons that have little to do with the platform that surfaced the target. A platform’s value should not rise and fall on a single trial.
CONVERGE’s true value lies in the data, the platform, and the compounding learnings across many programs. The right operating model lets that learning happen at industry scale.
What’s changed since we began
Two things, in particular, have changed in the last 24 months that have informed our new direction.
The first is that the market for our kind of data has matured. Demand has picked up sharply from biotechs, from pharma teams trying to understand failed trials, and increasingly from the AI labs training domain-specific foundation models. Public deals have started to set the floor: Lilly paid Chai roughly $30M for a three-year license to a structural biology model, Noetik and GSK signed at $50M over five years for an oncology model, and Tempus AI recently booked $150M in data licensing for foundation model development to AstraZeneca. A market is forming around proprietary biological data and the foundation models built on it, and CONVERGE has more of the relevant data in neuroscience than anyone else.
The second is that recent breakthroughs in AI architectures, the same ones powering ChatGPT and self-driving cars, can now solve a problem that has long held neuroscience back: how to make use of patient data that is incomplete and fragmented.
In cancer, patients can be matched to drugs because doctors can take a sample of the tumor itself, analyze it directly, and pick the right treatment. In neuroscience the disease happens inside the brain, which cannot be sampled while the patient is alive. The field has had to rely on indirect measurements — blood, brain imaging, spinal fluid — that capture the consequences of brain disease, not the disease itself. The only direct view of brain biology comes from autopsy. And the people who donate their brains are usually not the same people enrolled in clinical trials. The two halves of the picture come from different patients.
Older machine learning approaches couldn’t bridge those halves. They required every patient to have every measurement, so the field threw out most of the available data and worked with small, complete sets. Modern generative AI can. The new generation of models learns from incomplete data at scale, combines different types of information natively, and reasons about what is missing.
But the model only works if it has brain tissue data to anchor it. Self-driving cars hit a version of this. Tesla bet that cameras alone could carry the system. Waymo and others added LiDAR — a direct depth signal that camera readings could be calibrated against — and pulled ahead. Brain tissue is the LiDAR of neuroscience, and Verge’s decade of brain tissue data is what makes the architecture work: it lets us link brain biology, longitudinal blood, and clinical trajectory into a single picture of each patient, and reason about how that patient would respond to a given drug.
What we’re building next
Our first-generation platform helped us find new drug targets. CONVERGE 2.0 goes further and includes a new wave of generative AI models — what researchers call a “world model” — that builds an internal picture of a patient’s biology and uses that picture to predict how the patient will respond to a specific drug.
We are training multimodal AI models on a decade’s worth of proprietary human tissue data together with blood, spinal fluid, and clinical data from living patients. The model learns brain biology from tissue. It learns blood biology from living patients. Then it learns to connect the two from a smaller set of patients where we have both.
The output is what we call a virtual biopsy of the brain: a reconstructed picture of what is happening inside a patient’s brain, generated from a routine blood draw. From there, the platform is designed to:
Predict which patients are likely to respond to a given therapy
Predict who will experience side effects before the first dose
Predict how biomarkers will move in response to an intervention
Identify patient subgroups inside a single diagnosis whose underlying biology is meaningfully different
And eventually, run a patient forward in time: a virtual model of how their disease will evolve, and how different interventions would change that path
CONVERGE 2.0 is designed to catch exactly what CONVERGE 1.0 missed in our PIKfyve trial. Patient heterogeneity, predicted from a baseline blood draw before any patient is dosed. Brain state, inferred from the peripheral biomarkers we can actually collect from living patients. The direction a biomarker will move under a given intervention, predicted in advance rather than discovered after the trial reads out.
Together, these capabilities let us predict not just which targets to pursue, but which patients to pursue them in.
The business
Underneath all of this is a bet about where AI value will compound over the next decade. As general-purpose AI models hit the limits of the public text for new training data, the next leg of progress will increasingly come from vertical AI: domain-specific models trained on proprietary data, with the expertise to make sense of it. Life sciences is among the largest of those verticals.
High-quality multi-modal patient data will become as essential to drug development over the next decade as sequencing data was over the last one. AI labs need it to train their models. Drug developers need it to design better trials. Regulators are increasingly asking for it. The companies that supply that layer will sit at the center of how the field works, and that is the layer Verge has spent a decade building.
Drug developers can engage with CONVERGE today in whichever form fits their question. Some bring a specific translational problem (how their target behaves in human tissues, which patients to enroll, how to stratify a subtype) and we run CONVERGE against it. Some license drug targets we have already discovered in indications where they have downstream capabilities we do not need to build ourselves. Others license our data and AI models directly, or build new models with us. These are three doors into the same datasets, and every engagement adds to the underlying dataset. Over time, the data and AI layer itself becomes the primary product.
The vision
In the near term, we are working toward precision neuroscience: matching patients to drugs based on what is happening inside their brain, rather than what is visible at the surface. Smaller trials. Higher success rates. Drugs that work because they are given to the right people. This is the shift oncology made a generation ago, and it is overdue for the brain.
The longer-term vision is more ambitious. Once we integrate longitudinal data, our foundation models will be able to predict patient trajectories. A virtual model of each patient, run forward in time, to see where the disease is heading and how different interventions would change that path.
Transformational platforms are not built overnight. They come from persisting after setbacks and iterating on hard-won lessons. We come into this moment with a decade’s worth of tissue data acquisition and neuroscience drug development experience, a proprietary human data atlas across more than 6,000 patients, and the experience of taking an AI-discovered drug all the way through a clinical trial in patients. Few AI-native biotechs have lived that arc. It is the foundation Verge Labs is built on.
The work itself has not changed. Read human biology directly, in patients and in disease tissue, and use what you learn to design better drugs. What has changed is the scope. Doing this for one drug at a time means we will only ever learn from what we built ourselves. Doing it across a field means hundreds of opportunities to build knowledge around what works. That scale is what Verge Labs is built for.