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.

After almost four months, Mol-1 has officially dethroned OpenDDE to become the #1 open-weight antibody foundation model on cofoldarena.ai. Congrats @jiarlu and @MolenceBio team! All predictions are available to view and download.

Language models can learn new tasks from just a few examples in their prompt, with no retraining. This is called in-context learning (ICL). Now genome language models can do it too. With a handful of examples, Evo 2 ICL generates proteins, de novo antibodies, CRISPR-Cas9 loci, mitochondrial genomes and entire bacteriophages. We can even use the synthetic sequences iteratively to construct a chain-of-thought (CoT) and achieve harder design goals. New work with Yukun A. Hao, @BrianHie and team 🧵

🚀 Excited to share BindClaw! 🦀 Starting from a prompt or research paper, BindClaw connects biological mechanisms to protein design: identify targets and binding sites → build mechanism-specific computational assays → generate and assess binders → refine designs using the results. We use agents to evaluate whether designs fit the intended biological objective, including competition with a native partner and preference for a particular molecular state. 🧬 In a human-guided calmodulin campaign, 15 of 18 designs showed binding to calcium-loaded CaM, including subnanomolar binders supported by both BLI and SPR. This work is an incredible team effort together with @DidiKieran , @saint__ai, @ Kyle Gion and @sacdallago and many others! 📄paper/project: research.nvidia.com/labs/dbr… 💻code: will be released in a few weeks!

Our recent work on Claude-designed anti-IgE antibodies for food allergy was spearheaded by the brilliant @EErives40101 and @ferrara_em76515, follow them for potential future updates!

Public preview of BioEmu2! @MSFTResearch AI for Science’s new foundation model for biomolecular structure, dynamics and function. Open weights, MIT license. github.com/microsoft/bioemu2 Movie: BioEmu2-simulated binding of intrinsically disordered peptide PUMA to MCL-1 receptor. 🧵⬇️

Super exciting to see the $1.8B commitment to data collection towards the virtual cell led by @biohub @alexrives ! The best direction to take this investment IMO is to scale up (no pun intended) Thousandfold Expansion Microscopy from @eboyden3 @AliHShaib, enabling in situ, nm-resolution imaging of individual molecules in intact cells with simple light microscopy. Imagine blowing up a cell to the size of a marble and studying the identity, interactions, and even conformations of every individual macromolecule in a diseased tissue. This kind of full state observability will go a long way to closing the gap between observational and causal understanding (not to mention generating petabytes of high-SNR data for AI models).

When do biological reasoning models use their biological inputs? High performance can create a mirage of reliance on biological representations. Reasoning traces don’t always reflect the evidence. Our paper examines these limits and paths to better biological reasoning models.

Excited to share a preprint of my main PhD work in @nickpolizzi_'s lab - introducing LUCI-tag, a new orthogonal self-labeling protein tag based on K-Ras(G12C) and its covalent inhibitors! biorxiv.org/content/10.64898… (1/16)

Today, we’re announcing mBER-2, our latest AI protein design system, and sharing a bit of how we use it to explore biology in vivo at scale. For a long time, we’ve been working on what I think of as “frontier problems” in medicine and biology. We know a lot about the targets involved in disease, but there is still an enormous amount of biology we haven’t explored, and potential uses for that biology we haven’t discovered. One of those problems is drug delivery. There are thousands of potential targets, and most receptors remain unexplored as routes for delivering medicines. We want to understand which ones we can use, where they can take a drug, and what kinds of molecules make that possible. Ultimately, we need to test these molecules in vivo to understand what they actually do. We’ve spent the last six years building measurement technologies that let us generate millions of measurements in living systems. mBER-2 is an AI protein design system built for that scale of in vivo measurement. Our goal is to generate binders across the full range of targets we care about, while systematically exploring the possible binding sites on each one. It’s not just what you bind, but where and how you bind that determines whether a molecule performs it's function. mBER-2 lets us probe those differences at massive scale. We’re also sharing a look at some of our in vivo data. We’ve designed more than 50 million molecules across over 4,000 targets, and screened millions of molecules in living systems. By probing hundreds of receptors, we’ve found new pathways for delivering genetic medicines into fat that outperform industry benchmarks by a large margin. This is just the beginning. We call the space of all bindable sites across proteomes the EpiTome. As we explore it, we’re building the data to connect AI design, receptor binding, and what a molecule actually does in a living organism. Much like the idea of a virtual cell, we envision a Virtual Organism that helps us design medicines with specific properties in mind. The foundation is our own in vivo data, connecting molecular design to outcomes measured in living systems. The endgame is to use AI to explore more biology, measure what happens, and use what we learn to design better medicines. Every round should deepen our understanding of biology and improve our ability to build molecules that do what we need them to do.

Genomes are full of dark matter of unknown functions. We present Minerva, a new method for discovery guided by genome language models. Minerva reveals the interactions hidden in non-coding DNA, pointing to hundreds of new putative RNAs and repetitive elements per bacterial genome. Minerva allows us to find and study elements invisible to traditional methods at orders of magnitude greater scale than before.

Our wet-lab validation campaign for Proteina-Complexa is now on bioRxiv! It includes some new exciting experimental results, from large protein structures (fully codesigned!) to functional carbohydrate binders. biorxiv.org/content/10.64898… Thread with some of the additions 🧵1/n

I’m so excited to report that pLM-designed peptides work in vivo!! 🧬🐭 In our new @ScienceAdvances paper, a SaLT&PepPr guide directs selective degradation of pathogenic β-catenin in mice! A huge milestone for sequence-based binder design. 🌟 📜: science.org/doi/10.1126/scia… 🧵👇🏾

One of the puzzles in perturbation prediction, and the cause of many food fights, is that deep learning models often lose to the simplest possible baseline-- just predict the average response across all perturbations. This summer, multiple labs converged on an answer to this.

Breakthroughs in AI for biology are largely catalyzed by the diverse datasets deposited by scientists. To make experimental data on biomolecular dynamics from NMR as easily accessible as the PDB, @HWaymentSteele and I are releasing makeshift, installable via PyPi Preprint: biorxiv.org/content/10.64898… Docs: makeshift-docs.readthedocs.i…

Claude’s protein design results give insight into whether differences in success rates can be explained by structural confidence, or if there are latent factors. The evidence seems to favor the latter, suggesting that method improvements will remain challenging to judge in silico

Following the publication of our phage gen paper, I wrote some personal thoughts on biosafety and biosecurity as generative biology improves. Given the trajectory of progress, there should be concern and urgency -- but also optimism. brianhie.com/present_and_fut…

Dyna-1 is now published in @Nature! Since this preprint thread 18 months ago, there's prospective experimental validation and more! nature.com/articles/s41586-0… Many congratulations to coauthors @ginaelnesr @DorotheeKern and the whole team!