CEO Series | BigHat Biosciences: Building the zero latency lab for AI drug discovery

What if the best data in drug discovery is the data nobody publishes?

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In this episode of CEO Series, we sit down with Peyton Greenside, co-founder and CEO of BigHat Biosciences, to separate what's real in AI-driven biologics from what's noise, and to understand why the hard part of the problem starts after a molecule binds its target.

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Models and agents are commoditizing by the month. What doesn't commoditize is a lab that makes thousands of molecules a week, measures each one across 10+ properties, and feeds every result, including the failures, back into the next round of designs. BigHat built that lab first, six years ago, and has been running it ever since: roughly forty design-make-test cycles a year, on a proprietary cell-free synthesis stack that lets the team build antibody formats almost no one else can. The outcome is a company that treats binding, developability and function as one problem rather than three, and that has taken its first molecule into patients.

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In this conversation, we cover:

• Why two weeks of data from your own lab does more for a program than a year of progress in model architecture
• How BigHat designs biologics that encode logic, killing a cell only when it sees two targets, and what that unlocks for targets once considered too toxic to pursue
• Why you can't design a conditionally active quad-specific "out of thin air," and what a platform has to look like to engineer one
• How a company that can work on anything decides what to work on first, and why the pipeline pairs an internalization-optimized ADC with an avidity-driven T-cell engager
• What BigHat can do for pharma that pharma cannot do for itself, and what makes a partnership a success rather than a distraction

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Peyton also shares why generalization comes faster when you focus, how a team that is "bilingual and trilingual" across protein engineering, machine learning and clinical development actually works together, and what it means to become a clinical-stage company after six years of building toward it. BigHat recently announced a $75M Series C, co-led by Premji Invest and DFJ Growth, to advance its pipeline and scale the platform behind it.

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BigHat Biosciences is an AI-native biotechnology company that pairs frontier AI with an autonomous, high-throughput wet lab to design and develop next-generation protein therapeutics, with a clinical-stage pipeline and collaborations with Amgen, Merck, Johnson & Johnson, AbbVie and Eli Lilly.