AIxBIO at SynBioBeta 2026
Using AI to Make Biology Programmable
Biology and AI are converging to unlock entirely new frontiers. By bridging these worlds, researchers and innovators are building a shared toolkit spanning human health, food, materials, and planetary sustainability.
AIxBIO at SynBioBeta 2026 explores how foundation models and machine learning are being applied across DNA, RNA, metabolites, proteins, cells, and ecosystems. From multimodal models that link sequence to function, to simulations that capture biological dynamics over time, these approaches are laying the groundwork for a more predictive and programmable biology.
Therapeutics remain the most immediate and significant market opportunity, but the same tools and methods are shaping advances in materials, food, and sustainability. This is where the foundations for the next generation of programmable biology are being built.
Why AIxBIO Matters
Biology is complex. Static snapshots can’t capture living systems that change across time and scale.
Data is limited. AI is constrained by incomplete and noisy biological datasets.
Translation is hard. Predictions often break down moving from lab to real-world systems.
Intelligence is needed. From adaptive therapeutics to sustainable materials, biology requires models that can learn, adapt, and scale.
Who you'll meet
AIxBIO brings together the full ecosystem driving the future of AI and biology:
Innovators building the tools, models, and datasets that make biology programmable.
Industry leaders applying AI breakthroughs across medicines, food, materials, and sustainability.
Biologists, engineers, and investors converging to scale programmable biology for both human and planetary health.
What to expect
AIxBIO is more than talks — it’s an environment built for discovery, collaboration, and ideas.
Insights into how AI is transforming biology, from drug discovery to sustainable manufacturing.
Partnerships that bridge startups, academia, and industry.
A community building the shared infrastructure for programmable biology across health, food, and the planet.
The future of biology is programmable — powered by AI, shaped by collaboration, and built for healthy humans and a sustainable planet.
Confirmed Speakers
Sessions will include:
1
The next frontier of biology isn’t in predicting a single static protein structure, but in capturing how proteins move, fold, and interact across time and environments. This session explores how AI can illuminate protein conformations and dynamics, and extend those insights into virtual multi-cellular or tissue models. Experts will discuss the challenge of integrating heterogeneous datasets and instruments, and how breakthroughs in dynamic modeling could reshape drug design, disease understanding, and biomanufacturing. Can we build models that reflect the living, breathing complexity of biology—not just snapshots, but motion?
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2
Why do so many in silico models fail when moved to the lab or clinic? Too often, they’re trained on incomplete, non-human, or non-representative datasets. This session tackles the “data gap” head-on: from interoperability bottlenecks and the black box problem to the limits of current virtual cell simulations (~50 million perturbations vs. the billions biology demands). Panelists will explore how to create “human-first” datasets that reflect real biology, unlock mechanistic interoperability, and close the discovery–development divide. The goal: build AI tools that can directly identify viable drug candidates instead of stalling in silico.
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3
Drug discovery often measures biology at the cell level while interventions work at the tissue, organ, or whole-patient scale. This mismatch can make accurate cell-level predictions irrelevant in the clinic. This session dives into strategies to bridge that gap: multiscale modeling that nests single-cell dynamics within organ-level simulations, spatial transcriptomics that preserve context, and surrogate models that translate cell-level outputs into clinical biomarkers. Speakers will ask: how do we ensure virtual biology reflects not just what cells do in isolation, but how biology behaves in the real complexity of patients?
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4
For the first time, AI is enabling us to imagine medicines that “think” — turning on only inside diseased cells or under specific physiological conditions. This session explores how neural networks, trained on RNA and protein data, are unlocking programmable therapies with unprecedented precision. Imagine cancer drugs that remain inert until they meet tumor markers, or RNA vaccines that adapt to evolving viral landscapes in real time. The future of medicine isn’t static molecules — it’s intelligent, adaptive therapeutics
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5
From tissues morphing in development to microbes competing in a bioreactor, biology is inherently emergent. Multi-agent simulations — from platforms like BioDynaMo, CompuCell3D, and BIO-LGCA — are now powerful enough to model billions of interacting agents, capturing diffusion, metabolism, migration, and signaling with physical fidelity. Synthetic biologists are using these frameworks to probe design limits before moving to the lab, asking questions like: How far can diffusion alone carry a signaling molecule? What metabolic bottlenecks emerge in crowded cells? And how do engineered traits play out at population scale? This session will spotlight how agent-based models are becoming essential design environments for synthetic biology, helping teams test hypotheses virtually, anticipate failure modes, and translate biology into an engineering discipline rooted in predictive, quantitative simulation.
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6
AI x Bio isn’t just about technology — it’s about culture. Biologists must embrace engineering mindsets; AI engineers must respect biology’s messiness. Companies like Inceptive are pioneering apprenticeship models where talent learns both dialects — lab bench and codebase. This session explores how hybrid training is creating a workforce fluent in RNA structure, GPU clusters, and cellular pathways alike. The future of therapeutics depends on this new class of bilingual builders
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