Foundation models, virtual cells, AI-designed molecules, and the companies putting them to work.
SynBioBeta 2027 May 3-6, San Jose, California
George
Church
Harvard Medical School
Professor
Alex
Rives
CZI Biohub
Head of Science
Aliza
Apple
Eli Lilly
Vice President of Catalyze360 AI/ML
Patrick
Hsu
Arc Institute
Co-Founder
Stef
van Grieken
Cradle
CEO
Jorge
Reis-Filho
Foresite Labs
CSO, Operating Partner
Eli
Black
Xellsion.AI
Founder and CEO
Yogev
Debbi
Mana.bio
Co-Founder & CEO
Eric
Kelsic
Dyno Therapeutics
CEO & Founder
AIxBIO
Biology is becoming computable, and the consequences run the length of the field.
At the foundation, virtual cells and biological foundation models are replacing incomplete mental models with systems that predict rather than guess. Downstream, that predictive power is compressing the therapeutic pipeline: targets validated faster, molecules and proteins designed generatively, toxicity anticipated before the clinic, delivery engineered to reach the right tissue. Around it, labs are learning to run themselves, with autonomous hardware and AI agents closing the loop between hypothesis and evidence without waiting on human pace. And at the far end, where good candidates still fail, AI is reshaping trial design, patient stratification, and the real-world evidence that determines whether a therapy actually helps anyone.
This theme follows that arc from first principles to patient outcomes, and asks the question the field keeps circling: where is it real, and where is it still a promise?
Frontier Biology
Foundational and platform science reshaping how we understand and engineer biological systems — virtual cells, programmable metabolism, and foundation models for biology — ahead of any specific therapeutic application. Most biological research still operates on incomplete models of cellular behavior, forcing researchers to iterate expensively in wet labs rather than simulate and predict outcomes computationally. This subset covers the platform-level science aiming to change that: virtual cell models, foundation models trained on biological data, and programmable metabolic frameworks that will eventually underpin therapeutic, industrial, and agricultural applications alike, even though none of it is tied to a specific product yet.
Drug Discovery & Delivery
AI applied across the full therapeutic pipeline — target identification, molecule and protein design, hit-to-lead optimization, ADMET prediction, biomarker discovery, and delivery system design — for programs with a defined drug candidate or target. Traditional drug development remains slow and expensive, with most candidates failing in the clinic due to poor target selection, weak efficacy, or delivery limitations discovered too late in the process. This subset covers AI-driven tools compressing that pipeline: platforms accelerating target validation, generative molecule and protein design, predictive toxicology, and smarter delivery systems (LNPs, viral vectors, novel carriers) getting therapeutics to the right tissue reliably.
Autonomous Labs
Self-driving labs, design-build-test-learn loops, and AI agents that plan, execute, and iterate on experiments — automating the scientific method itself, across any stage of research or discovery. Scientific progress is still bottlenecked by human-paced experimentation: manually designing hypotheses, running assays, and interpreting results one cycle at a time. This subset covers the automation layer changing that, from autonomous lab hardware executing experiments without human intervention to AI agents that generate hypotheses, orchestrate multi-step research workflows, and close the loop between prediction and experimental validation — applicable to any domain, not just one indication or modality.
Clinical & Translational AI
Even a well-designed drug candidate can fail in the clinic due to poor trial design, the wrong patient population, or evidence gaps that only surface after approval — problems that happen downstream of discovery, not within it, and ones that patients and physicians experience directly. This subset covers AI applied to the clinical and translational stages of development: patient stratification and trial design, real-world evidence and outcomes prediction, biomarker-driven patient selection, and regulatory-facing AI tools — alongside the patient advocacy groups and physicians whose input shapes what "works" actually means in practice, bridging the gap between a validated target or molecule and a therapy that delivers real-world benefit.
Meet the Sponsors & Exhibitors
Why People Keep Coming Back
”
SynBioBeta is a who’s who of AI and biology. This is where the future is being built, so don't miss it.
Eric Schmidt
Former CEO
”
Nabla was accelerated into existence because of SynBioBeta. I met Seth Bannon from 50 Years, Cee Cee Schnugg from Boom Capital, and others from Y Combinator there, and those same people seeded Nabla. The vibe, leverage, and energy at SynBioBeta are unreal.
Surge Biswas
Founder
”
I met Algen at SynBioBeta and later invested in the company. It's exactly the kind of connection that makes the community so valuable.
Bill Tai
Co-founder
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For every single fund at Boom Capital, one of our best companies has come directly from SynBioBeta. I met Mammoth Bio at SynBioBeta, and I met Nabla Bio at SynBioBeta.
Celestine Schnugg
Founder
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