Matt McPartlon, co-founder of Chai Discovery, argues that generative AI models are beginning to bypass traditional waterfall preclinical stages by generating near-final drug candidates.
“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.”
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More from Matt McPartlon
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 McPartlonAug 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 McPartlonAug 11, 2026▶ 1:10:44🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
AssertionSupported
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 McPartlonAug 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 McPartlonAug 11, 2026▶ 25:33🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
AssertionSupported
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 McPartlonAug 11, 2026▶ 34:45🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
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.”
Matt McPartlonAug 11, 2026▶ 36:38🔬They Thought the Model Was Broken — Matt McPartlon & Neil Patil, Chai Discovery
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