Hands on: AI-Bio Masterclass
Learn from experts how to leverage AI to build with biology faster and cheaper
If your organization isn’t actively integrating AI into its workflows, you are falling behind. AI first companies are eclipsing the old wave and entering the market at an accelerated rate. This is your opportunity to catch up. Join us for a masterclass on AI for Biology.
During our AI and Bio Masterclass, you’ll have the opportunity to work 1 : 1 with the scientists, engineers, and product leaders driving AI adoption across the bioeconomy.
Over the course of our 3-day workshop, you'll learn how to translate real R&D challenges into structured AI workflows, use cutting-edge generative models to design and optimize proteins, enzymes, and pathways, and build AI-integrated experimental loops that accelerate validation and iteration across therapeutics, chemicals, materials, and food.
Wherever you are in your AI journey, our experts will guide you through practical, hands-on workflows that meet you at your current level and help you confidently apply these tools to your own R&D.
AI-Enabled Design & Discovery
Many teams struggle to explore massive sequence spaces that are impossible to search by hand. Experimental data is often sparse or inconsistent, making it hard to trust intuition alone. Stability, activity, and manufacturability predictions frequently break down when moving from in silico ideas to real-world systems.
Use generative models to propose novel protein and enzyme designs.
Score and prioritize variants for stability, activity, and manufacturability.
Uncover design opportunities impossible to find manually.
AI-Integrated Workflows & Data Infrastructure
Most teams struggle with scattered data and workflows that aren’t designed for AI. Experiments, assays, and fermentation runs often live in isolated systems, making it hard to create clean datasets or connect model outputs back to the bench.
Structure and standardize biological data so AI tools work reliably.
Connect ELNs, LIMS, models, and lab systems into a unified workflow.
Automate data capture and build reproducible AI-enabled experiment loops.
AI-Driven Experimentation & Optimization
R&D teams often run slow, linear experimentation that can’t keep up with growing design spaces or rapidly changing program needs. It’s hard to know which variants, conditions, or pathways to test first—and even harder to connect early data to meaningful optimization.
Use AI to propose, rank, and plan the next best experiments.
Apply model-guided DOE for fermentation, optimization, and scale-up.
Build closed-loop workflows that accelerate validation and iteration.
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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