Alex Rives
Head of Science, Chan Zuckerberg Biohub. On 2 shows, 2 appearances. The Shows tab opens the full record on each.
scientistfounderexecutiveacademic@alexrives ↗biohub.org ↗
Alex Rives pioneered protein language modeling by leading the Evolutionary Scale Modeling team at Meta FAIR and co-founding EvolutionaryScale. He serves as Head of Science at the Chan Zuckerberg Biohub, directing initiatives that combine large-scale AI with biological wet labs.
2shows
2appearances
28statements
15resolved
15supported
0contradicted
100%fully supported
4said about them ↓ Everything Alex Rives said on any show that made the record, most notable first. Each card names its show and opens the statement there.
ESMC model search generates novel antibodies achieving therapeutic-grade binding affinity levels
“What we're able to see is that, you know, you can search ESMC and you can actually find antibodies that are reaching the level of affinity that are, I should say, are really at the level of affinity that is needed for therapeutic function and activity.”
Rives: ESMC is state of the art among open models for multimer prediction
“Yeah, I mean, I think we're state of the art for open models.”
AI cell simulations will enable parallel reasoning across millions of biological hypotheses
“We're gonna have increasingly capable and accurate digital representations of molecules, genomes, cells, ultimately physiology. That's where you want to get. We're gonna have to go up that, that complexity scale, the levels of biological complexity that requir…”
Rives: Curing disease requires personalized computational models, not conventional pills
“What is, you know, what is the cure to disease look like, right? It's not a pill, right? It's not a medicine in the conventional sense. You know, it's going to have to be a system that is capable of modeling and understanding, you know, the underlying physiolo…”
Rives: Current Virtual Cell Models Cannot Predict Novel Interventions
“I think with, you know, kind of the current generation of models that are being called virtual cells, they are good representations of the underlying data, but, you know, they have a very limited ability to predict what will happen when you make a novel interv…”
Rives: Next-Token AI Models Can Learn Biological World Models from Data
“We have these systems that can basically kind of predict the next token and they can, you know, learn, World models from that. They can learn biology from the data.”
Alex Rives: Protein language models emergently learn biological structure and function
“One of the classes of models that we train are these protein language models. So they're really, you know, it's trained on the codes of proteins, and so anything they learn about biology is, is kind of emergent. And we've seen that they can learn things like b…”
Rives: ESM Fold achieves SOTA in protein and antibody interaction benchmarks
“And it's really, you know, 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.”
Scaling protein models by orders of magnitude unlocks emergent biological capabilities
“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 e…”
Sparse autoencoders found a single learned feature for nucleophilic elbows in ESMC
“You know, what we found basically is that the model has a kind of a single feature for this nucleophilic elbow and is activating across these like very evolutionarily diverse families, you know, really completely different structural topologies, proteins that …”
Incorporating metagenomic training data eliminated diminishing returns in ESMC protein foundation models
“And then, you know, what we saw basically is, is, is there are no longer diminishing returns to scale. So that's really saying that ESM two was kind of data limited rather than compute limited for ESMC.”
Rives: ESMC Has Been Used to Successfully Design scFv Antibodies
“We've been able to use this to actually now go and design many protein binders, but I think sort of most excitingly, we've been able to use this to actually design antibodies, SCFVs, and we're seeing really, I think, exciting success rates and a small number o…”
Rives: Sequence diversity teaches models structure while small variations teach function
“Having a vast diversity of sequences across a wide range of protein families is, you know, really critical for the emergence of this kind of structure Prediction capability, because I think kind of large diversity is what trains the model to understand, to dev…”
Alex Rives: Mechanistic interpretability will uncover biology inside protein models
“The hope is that you kind of really learn the underlying basis for how it's making the predictions, and so you open up the black box and you can actually understand kind of the biology that the model is representing.”
Rives: ESM Fold has predicted structures for over 1.1 billion proteins
“So we folded over 1.1 billion proteins and predicted their structures and identified kind of features connecting all of them through mechanistic interpretability.”
Rives: Meta FAIR trained the first protein transformer language model
“And so my team, when we were at MetaFair, trained really the first transformer language model for protein biology.”
Biohub predicted 3D structures for 1.1 billion proteins from global sequence databases
“So we put together kind of all the world's largest protein sequence databases. And so that kind of amounts to 6.8 billion non-redundant proteins, and then we've resolved predicted structures for 1.1 billion of those, and we've also computed features across all…”
Rives: Amino acid sequence patterns allow protein language models to learn biology
“The contexts in which an amino acid can occur are really determined by, you know, the structure, the function of the protein, its biological roles, you know, these I mean, very complex phenomenon both the intrinsic biology of the protein and its relation to al…”
Rives: ESMC Added Billions of Metagenomic Sequences Beyond UniRef
“ESM-II is trained on Uniref. And for ESMC, we added metagenomics. So we added billions more sequences to the training data.”
Feng Zhang's laboratory used the ESM atlas to discover novel gene editors
“Actually, the first version of the ESM atlas was used by Fang Zhang's group to find A new gene editing system.”
Rives: ESMFold 2 yields atomic resolution predictions in seconds without MSAs
“So the other thing about ESM fold two is a really fast model because it doesn't require the multiple sequence alignment. So You know, you can do inference kind of, you know, directly from the sequence it takes seconds, you know, you can get an atomic resolutio…”
CZ Biohub commits $500M to scale biological data creation and technology development
“We announced a few weeks ago, the virtual biology initiative we basically said, you know, we're gonna invest four hundred million internally in data creation and development of technology to scale data generation to be able to increase the number of modalities…”
Rives: Existing Tech Can Scale Biological Data 10x–100x Before Hitting Limits
“I think with current technology, you know, we can definitely kind of get, get data, 10 X to a hundred X where it is today with like relatively reasonable investments, you know, but then to get another 10 X or more in there, that's going to require, require a l…”
The other half of the tape: Alex Rives's own voice is left out of every number here. Other people bring the name up 2 times in 1 episode across the shows. 2 statements on the record name them. every mention, with the transcript →
Who brings them up most Mark Zuckerberg 2
Statements about Alex Rives, by other people (2)
Zuckerberg: EvolutionaryScale team joins Biohub under Alex Rives
“We're doing that by basically combining the team that we have, that's already put out all the models that we're talking about today, plus having the evolutionary scale team, which is just like very renowned join. And Alex is basically going to be running the p…”
Zuckerberg: Biohub leadership shifted from biologists to AI researchers
“Prior to Alex leading the effort, the previous leaders of the Biohub were basically primarily biologists who were interested in technology, right? And now I think we, this is the point where we really flipped that, right? Where, I mean, obviously you have a ba…”
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Latent Space 2
One line per show, most statements first. The link opens Alex's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| LATENT SPACELEDGER | Head of Science, Chan Zuckerberg Biohub | 1 | 22 | 100% 11/11 | full record on Latent Space → |
 NO PRIORSLEDGER | Head of Science, Chan Zuckerberg Biohub | 1 | 6 | 100% 4/4 | full record on No Priors → |