Advance Discount Ends Thursday, December 31
Foundation models, virtual cells, AI-designed molecules, and the companies putting them to work.
SynBioBeta 2027 May 3-6, San Jose, California
FemTech
The people building at the intersection of AI and biology, and the people funding them.
Xaira’s CEO and GSK’s SVP of AI are on the same main stage panel. Boltz’s founder is presenting alongside Novo Nordisk’s VP of Computational Drug Design. NVIDIA and OpenAI are in the building. So are the startups building foundation models, virtual cells, and AI-designed therapeutics, ingredients, chemicals, and materials that didn’t exist two years ago.
SynBioBeta is where the people building at the intersection of AI and biology present what they’ve built, and where the people funding, buying, and competing with them come to see it.
Where you’ll meet the people
building the next decade of women’s health.
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.
“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
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