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.
