Insight certainty 2/5 debate potential 2/5

McPartlon: Designing an antibody binder is often easier than predicting binding

Matt McPartlon · 🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery · Aug 11, 2026 · at 9:35

Matt McPartlon, co-founder of Chai Discovery, discusses structural prediction versus de novo protein design on the Latent Space podcast.

0:00 / 0:08exact quote · 8.8s
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“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.”

quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →

More from Matt McPartlon

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.”
Matt McPartlon Aug 11, 2026 ▶ 48:31 🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
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…”
Matt McPartlon Aug 11, 2026 ▶ 57:38 🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
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.”
Matt McPartlon Aug 11, 2026 ▶ 1:10:44 🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
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.”
Matt McPartlon Aug 11, 2026 ▶ 22:51 🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
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 …”
Matt McPartlon Aug 11, 2026 ▶ 25:33 🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
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.”
Matt McPartlon Aug 11, 2026 ▶ 34:45 🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
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