Assertion Not checkable as stated
McPartlon: AI models are nearing direct output of viable drug molecules
“We're kind of at the inflection point now. We're really seeing this internally at CHI, where the models are getting pretty close to, like, producing Molecules that could eventually, or like are very close to drugs.”
Insight
McPartlon: Multi-specific antibody modalities must be designed computationally from first principles
“There are drug modalities that you just can't discover with immunization. Like you're not gonna design your like crazy multi-specific Warheaded, super intense formats. These are really things where you kind of have to design these from first principles. Even j…”
Disclosure
McPartlon: Chai's research team largely lacks formal biology backgrounds
“Like the whole research team at CHI except for me and Kevin, really like we're the only people with quote bio background. Even still, like we're pretty far removed. So I think like we try to like look at every problem as a core ML problem.”
Assertion Supported
McPartlon: Chai-2 generated binders for 25 targets with 20% hit rate
“So we designed antibodies to 50 targets for that paper, got binders to about half of them with being on average around a 20% hit rate for binding.”
Insight
McPartlon: AI biology problems are solved like standard machine learning problems
“People think you can't work on like AI bio unless you're a biologist, but it's kind of like you can't work on like video models unless you're like a director or something. Like there are all these like super domain specific things like, oh yeah, to understand …”
Assertion Supported
McPartlon: Chai-2 achieved 0.33 angstrom error in cryo-EM testing
“And in this case, it was a 0.33 angstrom error, which is one third the width of an atom.”
Disclosure
McPartlon: Chai bet on model scaling over analyzing individual target failures
“Just be bitter, less impaled in that sense, and just really bet on the models getting better. And we definitely took the latter approach. Like we bet on the models getting better and we just pushed as hard as we could on that front.”
Assertion Supported
McPartlon: AlphaFold-Multimer gets antibody-antigen prediction right only 11% of the time
“Not really like outfold to got like, I think, 11%, the multiple version of this got like 11% of antibody antigen prediction cases. Correct. That means 90% of the time it's wrong.”
Insight
McPartlon: AlphaFold 3's high architectural complexity contradicts the Bitter Lesson
“One thing that I like to say is kind of like complexity and being bitter lesson pill, they're like fundamentally at odds. For example, I think like outfold three, I might get this number wrong, but I think it was like 23 sub modules. And at that point, that's …”
Insight
McPartlon: Designing an antibody binder is often easier than predicting binding
“And in some cases it might actually be even easier to design a protein binder that is an antibody than to actually predict how it might bind that target in general.”
Insight
McPartlon: Structure prediction is a speed-run benchmark compared to de novo design
“The nice thing about structure prediction is there is a ground truth that you can compare against. For design, you, don't really have that. You're like, here's some new, like disease molecule. Give me a binder for that. And like, if you want to know if this th…”
Assertion Supported
McPartlon: Triangle layers run inefficiently on modern GPU architectures
“These layers are pretty costly and like that kind of limits what you can do with the architectures. They're not like, Not only are they like costly in terms of compute, they're just like not efficient on modern GPUs either. You have small hidden dimensions, la…”
Assertion Supported
McPartlon: OpenAI co-led Chai Discovery's seed funding round
“Actually OpenAI co-led our seed round.”
Disclosure
McPartlon: Chai-2 was evaluated on targets with no known antibody binders
“We actually chose these targets specifically like to have no known antibody binder. So like if we did get a hit, like it was definitely the first antibody hit to this target.”
Assertion Partly supported
McPartlon: Chai prodigy hire Nathan Rollins entered Baker lab at 14
“One of our first hires on that realm was Nathan Rollins, who I think he started working in the Baker lab at 14. Graduated from Harvard at like 18 and got his PhD by like 21 or something like this in the Marx lab.”
