🔬ESM: The Bitter Lesson is Coming for Proteins - Alex Rives, BioHub

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Previously titled “🔬ESMFold2: The Bitter Lesson is Coming for Proteins - Alex Rives, BioHub” — renamed by the publisher on Aug 2, 2026

Latent Space: The AI Engineer Podcast 1h 10m 1 speaker 8 chapters transcribed 1 month ago

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What is the focus of the conversation with Alex Rives at the start of the episode?

Alex Rives 0:00

So ESMC is also approaching programmable biology, but I would say in a very different way. It's approaching it from this kind of world modeling perspective where the idea is basically you have a predictive model and, you know, you're going to search the world model to find protein molecules that satisfy kind of whatever design criteria that you have. So we've been able to use this to actually now go and design mini protein binders. But I think sort of most excitingly, we've been able to use this to actually design antibodies, SCFVs. Hello, welcome to the Latent Space AI for Science podcast. I'm RJ Haneke, CTO of Miroomics. Yeah,

Brandon Anderson 0:41

and I'm Brandon. Today, it's a pleasure to have Alex Reeves, head of science at Biohub. Yeah, would you like to introduce yourself real quick?

Alex Rives 0:48

Yeah, yeah. Thank you for having me here. It's great to be here. I'm head of science at Biohub. I'm a computer scientist, and I work on AI for biology, and a lot of my work has been on language

Brandon Anderson 0:59

models for biology. By the time this podcast is released, you will have put out several new, exciting, interesting models. Going over them, I couldn't help but have the kind of thought that you might be the most bitter lesson-pilled person in protein biology right now. Can you give a little context about what that means for biology and, you know, why you're so committed and excited to this route?

Alex Rives 1:23

Well, I'll take that. I believe in scaling laws. So, you know, I guess I've been working on this for, you know, since the summer of... And so my team, when we were at MetaFair, trained really the first transformer language model for protein biology. And so I guess, you know, I've always thought that there would be kind of emergence of biological information as you train a model to predict the next token that evolution creates. So our team has really explored that idea over a number of different years. And we've really kind of, I think, seen the scaling curve and really seen as we have increased models by an order of magnitude kind of in each generation that, you know, there's this emergence of new capabilities.

Brandon Anderson 2:08

Yeah, so you've been, you say, emergence of capabilities, scaling over generations. You've been working at this, as you said, for, I guess, would be eight years now or something like that. It didn't always work that way, right? Like there was signs that scaling might work. You know, we'll be getting to some new results where I think really you've kind of clearly demonstrated this hypothesis in a way that hasn't happened before. You seem to have a strong commitment to this in a way that I'm not necessarily sure I would have been so convicted that it would work in the same way. Protein language is not the same thing as natural language. There are similarities. If you start sampling a normal language transformer at a temperature, you're going to get gibberish.

Brandon Anderson 2:52

You sample a protein language model at infinite temperature. You're going to get something which is a valid protein, if not a not interesting protein. Despite the fact that it is a different domain for a different reason, I'm not necessarily sure that I would a priori assume the natural language model insight would transfer over. So what is specifically about proteins that you thought was special or, you know, that would make this also valid?

Alex Rives 3:17

Yeah, I mean, it's a really interesting question, I think, kind of a deep question across AI right now more broadly. And, you know, I think, you know, what's so interesting is AI right now is such an empirical science. And so we don't have, you know, theory that can always guide us in these things, but we have this really strong empirical evidence of scaling. The thing that I was motivated by is, you know, if you think about evolution and, you know, you think about the data that we have around proteins, we have databases that have billions of protein sequences. And, you know, those sequences contain patterns. And, you know, it had long been known, so, you know, this is going back, you know, decades kind of before, you know, we started working on this with language models, but that

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Chapters

8 chapters

1 What is the focus of the conversation with Alex Rives at the start of the episode? 0:00–8:43 2 How does Alex explain the scaling laws that drove the development of ESM‑C? 8:43–19:07 3 Why does training a protein language model on massive, diverse sequences enable it to learn structure without explicit MSAs? 19:07–27:01 4 What is the “world model” concept behind ESM‑C and how does it differ from traditional protein‑folding approaches? 27:01–36:26 5 How did the team build the 6.8 billion‑protein atlas and what insights does it provide? 36:26–45:21 6 How is ESM‑C being used to design mini‑binders, SCFVs, and therapeutic antibodies? 45:21–55:58 7 What is the vision for a “virtual cell” and how will protein‑protein interaction modeling evolve? 55:58–1:04:37 8 What actions does BioHub encourage the community to take with the open‑source ESM models? 1:04:37–1:10:05

Speakers

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Brandon Anderson 13 min

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Topics in this episode

Claude Shannon 1 Biohub 4 PDB 1 RJ Haneke 1 Brandon Anderson 1 ESM 1 Cell by Gene 1 ESM3 1 ESM2 2 Human Cell Atlas 1 Alex Rives 3 Virtual Biology Initiative 2

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Recorded27 May 2026

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Show notes

Editor’s note: In our first BioHub pod with Priscilla and Mark they discussed their acquisition of EvoScale, led by Alex Rives, who is now Head of Science at BioHub. With ESM-1 they trained language models on millions of protein sequences drawn from across life, with a simple “next token” objective: predict the amino acids that have been randomly masked out, based on the context of the rest of the sequence. But they soon found that these models also learned biological structure and function, including properties the model had never been explicitly shown AND that this ability scales predictably with compute, leading to ESM2 and ESM3.Today, Alex announced ESMFold 2, an open scientific engine to power prediction, design, and discovery across protein biology.Building on Cryo-EM data (discussed in the CZI pod), ESMFold2 reports state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics, and evidence that inference time scaling is also working across five targets in cancer and immunology.In a nod to that other famous AI x protein folding project, they are also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures, which you can play around with on their website. We are honored to work with them for this huge release!One of the refrains we’ve heard on the Science pod has been that protein folding, materials design, cellular biology, etc. are very different problems from Language Modeling. They definitely are. Yet Alex Rives and the ESM team at BioHub just released a preprint and model, demonstrating that vanilla BERT-like transformer models trained on sufficiently large and diverse data sets can beat specialized models like AlphaFold3 on some of the hardest protein-related problems. Andrew White had a great segment in our first LS-Science episode that explained how mind blowing AlphaFold2 was when it was released in 2020: it suddenly solved problems on a GPU on your desktop that DESRes had built custom-ASIC supercomputer clusters to solve. John Jumper and Demmis Hassabis received the Nobel Prize in Chemistry for this work.AlphaFold2 took advantage of an very clever observation: if multiple species co-evolve pairs of mutations, this implies that the mutations correspond to parts of the protein that are close in 3d space. This is usually shorthanded as MSAs (multi-sequence alignments), and is the key insight which makes AlphaFold2 so effective.Like other inductive biases, however, it hurts generalization.Scale-pilled before it was coolIf you take a look at the timeline for scaling laws for LLMs and release of structure prediction models, the ESM team notably doubled down on their MSAs-be-damned approach after AlphaFold2 released. This obviously requires a great deal of belief in the scale hypothesis.Why the conviction?ESM developed at a time when many of the scaling laws and the “Bitter Lesson” were proving increasingly correct. AlphaFold2’s wild success must have been both exciting and bitterly disappointing. But using MSAs mean that the model is is dependent on training data that contains MSAs in order to be accurate in a given domain. For things like antibodies that don’t have MSAs to train on, AlphaFold tends to do poorly.ESM takes a different approach: learn the relationship between different proteins by unsupervised training on as much diversity as you can find (sound familiar?) and then correlate that back to structures know from the Protein Data Bank (PDB) and other sources. In other words, a World Model.World Model for proteins“World Model” is a hype term that I define like this:Use unsupervised training to learn abstract patterns from the data:* The abstraction should be semantic - novel constructions represent things that obey the rules of the real world* The abstraction should be compositional - recombining different patterns leads to novel and often valid constructions* The abstraction should support generalization - it predicts things in the real world it wasn’t trained on Once you have a world model, you can attach “heads” to it for downstream tasks: predict properties of a protein, decompose its functional features, or search the representation for proteins that meet design criteria. The two big models BioHub just released under MIT license map directly onto this:* World model → ESMC (a model trained on 2.8 billion sequences)* Structure-prediction head → ESMFold2One of the interesting ways the world model can “predict things” is to generate proteins sequences and then measure the predicted properties, such as binding affinity, in the lab. Alex talks in the episode about validating some of the harder molecules they predicted in the wet-lab. Very cool!Another way is to use mech-interp techniques such as Sparse Auto Encoders (SAEs) to extract semantic features from your model, and then find novel features that predict unknown biology. I won’t spoil this part for you: it was one of the highlights of the episode for me!A cell is a computerWe have all heard that genes are like computer programs, but usually the analogy fizzles after that. Of course genes are transcribed into RNA and RNA is translated into proteins, so genes are programs for building proteins, but that carries the analogy only to “binary digits are programs.” Here’s a better analogy: you can think of the cell nucleus as a storage device / storage controller, the ribosome as a JIT-compiler and runtime, and the semantic features that we learn from our world model via SAEs as functions, proteins as processes that interact together in workflows (signalling pathways) to produce behaviors and outputs (phenotypes). Like functions, the SAE features have a hierarchical composition from local, secondary and tertiary structures (mimicing protein structure), but also motifs that are conceptual, such as membrane integrations, disordered regions and disulfide bonds. As we learn to compose these features we into novel protein designs, we move further towards programmable biology. Alex goes into much more detail about this in the episode, as well as:* Principles for new data collection* BioHub’s vision* Modeling the cellEnjoy!Full Video podcastplease like and subscribe!* X: https://x.com/alexrives* LinkedIn: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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