Nan Li retweeted
Today, in collaboration with Anthropic, we’re excited to share rapid initial progress towards an antibody treatment for food allergy, which affects over 200 million people worldwide, discovered with the help of Claude.
At Divergence Labs, we believe that AI can unlock the next generation of antibody therapeutics with more effective mechanisms than before. Such mechanisms are urgently needed in food allergy, which involve overcoming one of the tightest affinity protein pairs in immunology, IgE and its receptor FcεRI, on mast cells.
Treatment with Xolair—the only approved antibody for food allergy—sequesters circulating IgE, but mast cells remain sensitized for weeks to months due to the extraordinarily slow dissociation of IgE from FcεRI.
We envision a treatment that actively pulls IgE off of FcεRI, without waiting for natural dissociation. Like picking a lock, this requires the right combination of geometry and force.
Our research team, working with Claude, ideated on mechanisms to accomplish this disruption, and converged on an antibody format with two arms with different functions and epitopes. Within one month, we designed, synthesized, and experimentally confirmed the biophysical function of the two arms, which we are now assembling into full therapeutic formats.
Nan Li retweeted
So pumped to share our third volume of the @_DimensionCap Research magazine!
These articles were enjoyable to write alongside my research colleagues @SimonDBarnett and @bauer_lesavage
In this volume, we dive deep into the foundations and applications of AI models, from pre-training, post-training, and agentic use to the emerging bio data market.
This volume also features my first research article on applying autoresearch for training machine learning models to predict protein thermostability. I assessed how frontier models approach hyperparameter tuning and discussed how such an approach can be synergistic with traditional Bayesian optimization sweeping and expert-guided search.
Since the article’s publication in April, we have seen a tremendous amount of progress in recursive self-improvement for closed-loop scientific discovery, and this is only the beginning!
Our goal here is to present these broad topics in a rigorous yet accessible manner for everyone, no matter if you’re a frontier ML researcher or a weekend biotech warrior. We very much hope you enjoy!
Nan Li retweeted
Excited to share our third volume of the @_DimensionCap Research magazine!
These articles were a blast to write with my research colleagues @SimonDBarnett and @ChemVagabond, and I feel so lucky to get to brainstorm, debate, learn, and write about these topics alongside them both on a daily basis.
You’ll probably notice a theme to a lot of these articles that we have written throughout 2026: the chronicles of us diving head first into AI model training, the data needed to feed them, and emergent applications of training at the frontier.
One of my favorite articles I wrote this year was centered around the bio data market and why biological training data has such unique considerations compared to text data. Since I published this one back in June, the demand for high-quality, therapeutic-relevant bio data has become even more obvious, and I don’t see this demand slowing down. The impact that AI is and is going to have in bio is becoming increasingly apparent every day, and we can’t get there without the right datasets.
Our goal here is to make these topics accessible and interesting to all, no matter if you’re a frontier ML researcher doing this work daily or brand new to the space and just trying to get a sense of it all. We very much hope you enjoy!
Nan Li retweeted
That's a wrap! Today, we're (@_DimensionCap) releasing the third volume of our Research Magazine.
Before anything else, I want to shout out my tremendous research colleagues Bauer (@bauer_lesavage) and Frank (@ChemVagabond), who were both core contributors to this volume.
We're also releasing a digital version:
dimensioncap.com/research-ma…
~~~
Compared to previous magazines, our third work is more heavily focused on machine learning. That's by design.
Dimension invests in all companies, small and large, spanning the continuum from compute to the life sciences. At our core, we love technical teams working on the hardest, most important problems.
That means data center infrastructure, new networking protocols, new silicon, inference optimization, physical AI, lean/tech-enabled biotechs, data generation, and more.
As such, we challenged ourselves to go deeper into the compute stack than ever before, and how it connects to various branches of science. This magazine is a small sliver of the proverbial 'research exhaust' that our team generates over the course of a few quarters.
~~~
One piece I want to highlight is Without Form and Void, which was my attempt at unifying my background in fluid mechanics, my admiration of clever inductive biases in ML, and a major industry chokepoint seldom talked about in CS circles, which is bioprocess scaling.
It's also the first piece I've tried writing with this much history (back to Galileo).
~~~
We hope you like Vol. 3 and we can't wait to show you all what's in store in Vol. 4!
Nan Li retweeted
At the start of this year, @joshim5 told me 2026 would be the "year of deployment" for @chaidiscovery
That wasn't just an aspiration — Chai has now signed its 6th pharma deal in the past 9 months:
Lilly
Pfizer
Novartis
argenx
BMS
and now with GSK:
endpoints.news/chai-discover…
Nan Li retweeted
Folding models, protein diffusion, single-cell LLMs, and agent swarms: AI for science runs on a different model every hour and needs a different number of GPUs every day.
Listen to leaders in computational biology from @GSK, @AnthropicAI, and @achira_ai on the main stage at Runtime.
Nan Li retweeted
Frontier drug discovery programs require frontier capabilities. Today, we’re excited to share that BMS will be using Chai’s models to support the discovery of new therapeutics. Since rolling out Chai-3 to partners at the start of this year, we’ve partnered with @EliLillyandCo, @Novartis, @Pfizer, @argenxglobal, and now @BMSnews. Many of the world’s leading biopharma companies are now using our models to go after diseases.
Scientific discovery is the domain where access to frontier AI matters the most. At @ChaiDiscovery, we’ve seen first hand how our latest model generations can make the difference between discovering a molecule and not. We’re excited to work with @BMSnews to break new ground in medicine and science.
Nan Li retweeted
we believe biology is AI's true frontier edge case
today we're it's launching the capital map: the top VCs in AI x Bio
The TechBio 50
Nan Li retweeted
Agents are your new users. Learn from them.
AX Cloud is now in research preview: managed infra, agents and data for experimenting with coding agents as users.
Run unbiased simulations of coding agents at scale. This allows you to understand how agents discover and use products. Yours, your partners' and your competition's. Use this to optimize your product, craft go-to-market materials or develop your technical partnerships.
Get started for free on managed infrastructure, then upgrade when you're ready to parallelize at massive scale. Every run starts from a fresh sandbox. No bias. No context pollution. No cheating.
Run your first experiment in minutes: docs.514.ax/getting-started
Nan Li retweeted
I've joined @_DimensionCap as an operating partner.
I'll be building cool stuff with people I love and trust.
Here's a bit more about what I'll be doing and why I chose to join this incredible team for this season of my career.
linkedin.com/pulse/climbing-…
Nan Li retweeted
joining @_DimensionCap was just an elaborate ploy to infiltrate @Getthecheckpod and become their intern
@anikamirzaa @priya_kamdar @mayamshahh
.@nikhilvnamburi went from tracking down the investment fund behind his college scholarship to a 5 person investment team that manages $1.65B @_DimensionCap
They just announced an $800M Fund III. They’ve backed @AnthropicAI, @modal, and @chaidiscovery
We sat down to talk about:
> Why only 1-2 investors matter at every fund (except Dimension)
> If the Coefficient Bio @AnthropicAI acquisition returned fund II
> Why hyper-concentration freaks out normal VCs but excites him
If you watch till the end he flips the interview on us (first time a guest has ever done that!)
00:00 Dimension raises $800M Fund III
06:29 The unexpected way Nikhil found venture as an engineer
08:48 Who are the LPs behind huge venture funds
11:04 Why Dimension only has 5 investors
13:14 Coefficient returned the fund?
14:49 They predicted bio would be big
16:34 Does Nikhil believe in AGI
17:20 Does Nikhil believe in looksmaxxing
20:10 Hot take on if SF VCs are smart
21:53 How to break into VC
22:52 Why only 1-2 investors matter at each fund
23:35 Churn and burn in the junior VC role
34:50 His viral quant thesis
42:11 Nikhil interviews us...
46:45 Biggest recession indicator
53:24 Nikhil is the podcast’s most eligible guy friend?!
Nan Li retweeted
We weren’t happy with the existing drug pipeline and catalyst databases, so we built a new one designed to be easy for both humans and LLMs to use.
We’re making it freely available today as a community resource. The sign-up link and blog post are in the thread below.
Nan Li retweeted
"We are witnessing a revolution of what it means to be diseased."
@zavaindar believes biotech is entering a new era where conditions once dismissed as behavioral or insignificant are becoming major targets for new drugs.
"10 or 15 years ago, pharma and biotech didn't invest in [obesity] because they didn't consider it a disease. They considered a behavioral problem. Today, it's considered a disease and [GLP-1s] are the largest class of drug on the planet."
"In areas like aging and longevity, inflammation, and immunology, 20 years ago, no one would touch that because skin related diseases weren't considered serious enough. So we are starting to see a redefinition of the types of areas where biotech and pharma can actually play."
Nan Li retweeted
Today we're unveiling Dimension III: a $800M fund to partner with the leading entrepreneurs across the frontiers of compute (silicon through infrastructure) & science (biotech through physics).
To our founders, past, present, and future: now is your time. LFG!
Nan Li retweeted
fun to see this factoid of largest biotech financings in 2026. Dimension 1 was the first outside (lead) capital in both @newlimit and @chaidiscovery during the "biotech winter" of 2023 and 2024
Nan Li retweeted
a few years ago we office’d @joshim5 @jackdent and the early @chaidiscovery team in our noho hq as they traversed the 0 to 1 of newco genesis. alongside thrive and OAI we led the first financing into the company
today, as CHAI sets the bar for generative molecular design - inking partnerships with Lilly, Pfizer, and Novartis - @_DimensionCap is writing the largest check to date in our 3 year history, helping to lead a $400M financing with index, kleiner, and sequoia
at the limit, preclinical (and increasingly clinical) drug discovery and development will never be the same 🚀
Today we’re launching @chaidiscovery, led by @joshim5 & his polymathic world class cofounders. We’re also open sourcing Chai-1: a state of the art frontier model for biomolecular interactions. @_DimensionCap has co-led the founding round alongside Thrive & OpenAI
Nan Li retweeted
Thrilled to announce our $400M Series C led by @IndexVentures, with @kleinerperkins, @sequoia, @_DimensionCap and others.
Great to speak to @m_delamerced @nytimes about how drug discovery using AI is moving from a promise to actual deployment.
Nan Li retweeted
I'm delighted to announce a new collaboration between @ChaiDiscovery & @Novartis.
It's been a pleasure working together over the past year, and we're excited to scale our collaboration to support broader deployment of Chai-3 across multiple therapeutic programs.
Nan Li retweeted
Since two of these benchmarks are provided by us @Dyno_Tx , I can confidently comment.
- The AAV one is uncontaminated private data of a viral trait that we've been measuring for years (i.e. sharing publicly will defeat its purpose), and use it every day (required not sufficient) to design capsids that actually do work in primates. This performance provides a real lift in expertise and resources needed to do viral evolution.
- The black-box sequence design challenge is our take-home interview. Fable completely outperforms the best people in the field on given the same exact instructions as humans, again in a private task not seen on the internet. We will release this task publicly at some point.
Biology is slow. Real accomplishments take time because the bottlenecks downstream are meaningful.
But dismissing both the upside and the risk is highly premature. They've accomplished about as much as you could imagine. In particular, being wrong on the risk is extremely costly. I for one am the most excited I've ever been that bio is going to be transformed, and agentic reasoning is a big part of why.
Also the "unknown company" is actually very well known among experts because they are all very smart/accomplished people but are not hype-y on the twitter-verse.
