# ESMFold2 makes protein design a digital-first process

Markdown copy of https://realalpha.app/s/esmfold2-makes-protein-design-a-digital-first-process/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Alex Rives](https://realalpha.app/builders/alex-rives/) · Head of Science, Biohub · Created the ESM protein language models at Meta and co-founded EvolutionaryScale, now leads Biohub's AI-for-biology science.
- Host: [Elad Gil](https://realalpha.app/people/elad-gil/) · CEO, Gil & Co · Investor in Airbnb, Stripe, Coinbase and Anthropic; co-founded Color.
- Host: [Sarah Guo](https://realalpha.app/people/sarah-guo/) · Founder, Conviction · Ex-Greylock partner; early investor in Cognition, Harvey and Mistral.
- Show: No Priors
- Episode: [“Curing All Disease by next century is too conservative" - Mark Zuckerberg](https://realalpha.app/v/u8cYwaLzN-k/), Jun 10, 2026
- Moment: 28:00 to 32:13 of the episode (4 min), playing on https://realalpha.app/s/esmfold2-makes-protein-design-a-digital-first-process/; the episode from there: https://realalpha.app/v/u8cYwaLzN-k/?t=1680

## Quote

> “And we found **nanomolar binders** there. And so, you know, that's really the level for therapeutic activity.”
> — [Alex Rives](https://realalpha.app/builders/alex-rives/) at [30:13](https://realalpha.app/v/u8cYwaLzN-k/?t=1813) · No Priors

## Takeaways

1. The model folded more than 1.1 billion proteins.
2. Protein design emerged from a general model of protein biology.
3. A 96-well experiment tested candidates chosen digitally.

## About this segment

Alex Rives explains how ESMFold2 predicts protein structures and supports antibody design, then validates candidates in the lab. A small experimental screen can test designs selected digitally from a much larger space.

From ““Curing All Disease by next century is too conservative" - Mark Zuckerberg”, Jun 10, 2026. [Watch the full episode](https://realalpha.app/v/u8cYwaLzN-k/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[28:05](https://realalpha.app/v/u8cYwaLzN-k/?t=1685) Maybe I could say something about our most recent launch, because I think it also kind of illustrates. Oh, yeah, please, we should ask you explicitly about it. Yeah, so, you know, because I guess it was just a week ago about now. So we announced the new ESM fold. And so this is basically an open system for scientific discovery in protein biology. It's a world model of protein biology that's been trained.

[28:31](https://realalpha.app/v/u8cYwaLzN-k/?t=1711) It's a language model base. So it's been trained on billions of protein sequences, kind of learns these emergent representations of protein biology. And then we can use it to make predictions of atomic resolution protein structure. And we can use it to, and it's really fast. So it's blazing fast. So it's kind of, you know, illustrating this Pareto optimal frontier of kind of speed and accuracy in structure prediction.

[28:57](https://realalpha.app/v/u8cYwaLzN-k/?t=1737) And so this allows us to kind of characterize, you know, really vast kind of stretches of the protein universe. So we folded over 1.1 billion proteins and predicted their structures and identified kind of features connecting all of them through mechanistic interpretability. But I think the thing that I thought was most exciting about this model is it's this really general model of kind of protein biology. And so you can use it as a world model. You can actually really start to search the space of the world model to design new proteins.

[29:31](https://realalpha.app/v/u8cYwaLzN-k/?t=1771) And it's really hitting state-of-the-art across pretty much every structure prediction benchmark, and especially on protein-protein interactions and protein-antibody interactions, which is really critical for therapeutic design. And so what we found is you can actually now use the model to design proteins and to design actually single-chain antibodies. And so you can do all of this digitally and then really...

[30:00](https://realalpha.app/v/u8cYwaLzN-k/?t=1800) A small number of experimental trials, basically like a 96-well plate, select from hundreds of thousands of trajectories digitally, actually synthesize 96 proteins, test them in the lab in a really kind of short, easy experimental cycle. And we found nanomolar binders there. And so, you know, that's really the level for therapeutic activity. So it's really, I think, showing that you can have these kind of general purpose models that can, you know, we didn't design a model for antibodies. We didn't design a model to, you know, to be able to bind one particular target.

[30:39](https://realalpha.app/v/u8cYwaLzN-k/?t=1839) You know, we just designed a model that could understand proteins and you kind of get protein design as an emergent property. And then I also think it illustrates this kind of the power of open science and open source because, you know, we release this as basically an open discovery engine. And so really anyone can build on it. And so it takes what are these really intensive laboratory experiments where, you know, you have to screen through hundreds of thousands or millions of antibodies and high throughput screens in the lab.

[31:10](https://realalpha.app/v/u8cYwaLzN-k/?t=1870) And, you know, you can really just kind of spin up an instance and compute and now, you know, be able to generate antibodies. You should say more about sort of like we took that data when we looked at an antibody screen and then we validated, we looked at PD-L in cells and then we looked at it under the cryo-EM and sort of how all that complemented, validated what you were seeing in the models. That's right. Yeah. So, I mean, I think it's really critical, you know, to actually go and characterize these molecules in the lab.

[31:41](https://realalpha.app/v/u8cYwaLzN-k/?t=1901) And it's, you know, we have a structural biology center here. We have incredibly powerful cryo-EM microscopes. And so we're really able to kind of look at these proteins biophysically and functionally. And so, you know, we designed proteins for several therapeutically relevant targets. And we're able to confirm their function. It's delightful when it works the way it's supposed to. Yeah, that's very amazing.

[32:09](https://realalpha.app/v/u8cYwaLzN-k/?t=1929) We're able to look at the structure also.