why aren't all 11 resolved? a statement only gets an assessment when the public
record can support or contradict it. opinions and what-ifs never can, and 0 checkable
ones are still open, waiting for their date. predictions held up or didn't;
assertions are supported or contradicted. on every card:
▮▮▮▮▮ certainty ·
▮▮▮▮▮ debate potential. speakers are clickable
Assertion Supported
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.”
Assertion Supported
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.”
Assertion Supported
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 …”
Assertion Supported
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.”
Assertion Supported
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…”
Assertion Supported
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.”
Assertion Supported
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…”
Assertion Supported
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.”
Assertion Supported
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.”
Assertion Supported
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…”
Assertion Supported
Rives: The ESMC family includes 300M, 600M, and 6B parameter models
“So there's actually three models in that family. There's a three hundred million parameter model, a six hundred million. Parameter model and a six billion parameter model.”