Cradle helps biologists design improved proteins in record time using powerful prediction algorithms and AI design suggestions.
Project leadVisualize, analyze, and predict candidates’ potential all in one place. In other words, know what works out in the real world – faster.
Train the Cradle AI with results from your wet lab to build a tailored model that improves candidates with each round of testing.
Use Cradle for any project. No high throughput assay? No problem. Get impressive results between rounds with just a 96 well plate.
Choose what to improve, then generate quality candidates in just one click. Not feeling the results? Click again.
Predict a protein's 3D structure, generate new sequences with improved thermostability, optimize codons and much more soon...
Predict the structure of an individual sequence or multimer with AlphaFold 2. Optimized for speed without compromising on accuracy.
Optimize the thermostability of your proteins with models trained on large datasets. Get even better suggestions after a round of experiments in the lab.
Optimize your protein’s codons for better expression. Go beyond codon usage tables and leverage machine learning models that have seen DNA-Protein-sequence-pairs from your host.
Design proteins that do better in harsher environments by improving their stability in solvents.
Want your enzyme to go faster? Improve the rate by which your enzyme performs its reactions.
Design proteins that fit like a glove by optimizing their specificity.
Improve your protein’s affinity to increase protein-protein interactions.
Optimize all of the above by setting targets for multiple phenotypes simultaneously and let the models find an optimum.
Cradle keeps your sequences and data private and secure while giving you full ownership of all intellectual property.
We think your sequences are incredibly valuable, but we’ll never know. Why? Only you and people you invite from your organization can access sequence data.
As industry veterans, we know it all comes down to security. Cradle uses bank-grade level of security to keep your data safe.
We keep IP a lot simpler than we do security. How simple? It’s yours, you own the IP. How about royalties? Nope. You only pay for users and usage of our tools.
We believe cells can theoretically produce almost anything, from fabrics to food, fuel and medicines. Designing cell-factories is hard, but our tools and machine learning models can make it easier. Our mission is to ultimately help replace traditional farms and factories for a more sustainable world.
Jelle makes sure that Cradle's products are easy to use.
He previously designed the first apps for Uber, Booking.com and CataWiki.
Elise turns machine learning-generated sequences into physical proteins and tests how they behave.
She previously engineered microbes in high throughput at Zymergen and Perfect Day. She holds a PhD in fungal synthetic biology.
Sytske finds out what our users need and learns from their feedback on our tools.
She was previously researching people at Uber and Facebook/Meta and teaching statistics in San Quentin Prison. She holds a PhD from the University of Cambridge.
Tomasz makes the machine learning workflows available to the end users through the Web-app and APIs.
He was previously front-end lead at Brainly and software engineer at Google and Base CRM.
Martin teaches Cradle’s algorithms how to design even fancier proteins.
Before joining Cradle he worked at Disney and JP Morgan. He holds a Masters degree in Computational Science and Engineering from ETH Zurich.
Daan tests if our AI generated proteins actually do what they are supposed to.
Previously positioned at Nostics, he utilized lasers and nanotech to nurture an assay based on surface enhanced Raman scattering for the detection of COVID and bacteria.
Harmen automates the lab.
He previously engineered microbes at DSM and Zymergen. He has a PhD in metabolic engineering from Delft University of Technology.
Luba pairs up with Stef on doing everything else that needs to be done (and some more).
Before joining Cradle, she built teams at RepRisk (ESG data), ran projects at WWF, and consulted for BCG. She holds a law degree from McGill and an MBA from Queen's (Canada).
Franzi teaches large language models to understand proteins.
She was previously a machine learning engineer at Google, IBM and Oracle and did computational biology research at MIT.
Ena is a machine learning engineer dabbling in some fullstack development as well.
She studied molecular biology and has a Masters degree in mathematics and computer science.
Adam builds easy to use and responsive interfaces for people who want to use our tools from a browser.
He was previously a frontend engineer at Hiya, IBM Video Streaming and Wunderman Thompson Budapest.
Arthur loves solving puzzles, and considers protein design to be the most worthy puzzle of all!
He was previously in Novartis where he worked on diverse life science projects using machine learning and holds a masters degree from Ecole Centrale Paris.
Eli makes sure our models and algorithms are doing what we think they are doing, and he keeps an eye out for the latest and greatest techniques in the literature.
He was previously at Google (Brain, Accelerated Science, Cloud) working on biological sequence design, AutoML, and natural language understanding. He studied mathematics, computer science, and biochemistry.
Daniel is the adult supervision. When the kids are good, he squeezes in as much coding as possible.
He previously worked at the Medical Bioinformatics Lab at ETH, he was CTO at Daedalean AI and Senior Staff Eng at Google. He holds a PhD in applied mathematics (genetic algorithms).
Max decides where buttons go, what they do, and how they look.
He previously helped design user interfaces at Klarna, Citrix and Leo Pharma.
Emily builds DNA, uses microbes for protein expression, and tests protein behavior.
She previously worked on protein expression, purification and refolding at Zymergen, as well as NGS target enrichment at Twist.
Stef does everything else that needs to be done.
He was previously part of product leadership at Google Research & Machine Intelligence. At Google X his focus was on AI infrastructure and developer tooling applied research.
We are funded by leading investors Index Ventures and Kindred Capital, as well as long list of world-class advisors and angel investors.
Read funding announcementIndex helps entrepreneurs turn bold ideas into businesses that have a long-lasting positive impact on the world.
"We love Cradle because it is enabling a new wave of progress that aims to improve the health of both humanity and the planet."
Kindred is an early investor in LabGenius, Ori, Eleven Tx, and others.
"We believe the next $100Bn opportunity will be built at the intersection of biology and computation, and that Europe can play a leading role on the global stage."
CEO of Twist Biosciences
SVP AI at GSK, former Head of Product at Deepmind and co-founder of Alphafold project
Founder at Mollie & Messagebird
CEO & Founder at Lyft
Chief Scientific Officer, Perfect Day
Director of Product at Deepmind
Professor at Berkeley & Founder of Arc Institute
Honorary Chairman and former CEO at Royal DSM
Founder & CEO at DNA Script
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