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Cradle raises $73 million to accelerate adoption of AI-powered protein engineering

The investment will help Cradle accelerate its mission to empower millions of scientists to engineer more sustainable products and better therapeutics on smaller budgets and with higher success rates.

Published on November 26, 2024

Image: cradle

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Cradle, a platform for AI-powered protein engineering, has raised $73 million in Series B funding to respond to rising demand for its technology in R&D-intensive industries. The round was led by IVP, with participation from previous investors Index Ventures and Kindred Capital. IVP is a North-American investment company, but has a special fund for European tech startups. Today’s funding brings Cradle's total raised over $100 million.

Using Generative AI unlocks the impact of engineered biology

Proteins are at the core of many products, from pharmaceuticals to food. These molecular machines can reshape our world— enabling life-changing therapeutics, animal-free foods, eco-friendly pesticides, oil-free chemicals, and more. Developing new proteins is critical to unlocking more innovative, sustainable solutions to our toughest societal and planetary challenges, from fighting climate change to transforming healthcare and manufacturing.

However, traditional research methods for engineering better proteins have been slow, expensive, and unreliable. A successful R&D process can take years and millions of dollars, and many projects are never completed successfully because they cannot achieve their targets.

Cradle's AI platform enables scientists to accelerate the discovery and development of improved proteins by making engineering better proteins significantly more accessible, faster, and more cost-effective. The key to Cradle's success lies in its AI platform’s unique approach, which allows it to reduce the number of experimental rounds needed dramatically.

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Cradle accelerates protein research & development as much as ten-fold

Cradle has experienced rapid growth and is now in commercial deployment across a wide range of industries. In the last year, It has significantly expanded its customer base and impact, signing new partnerships with Novo Nordisk and Ginkgo Bioworks and expanding its customer base to include pharmaceuticals, chemicals, food, agriculture, and materials. Cradle’s platform has been shown to accelerate research and development timelines considerably.

Stef van Grieken

Stef van Grieken, image: Cradle

Stef van Grieken, Cradle’s CEO and co-founder, said: “Cradle was founded on the belief that we could solve global planetary and human health challenges by using generative AI to accelerate the development of bio-based products rapidly. Over the past two years, our research and our collaborations with partners have proven that this technology can deliver remarkable results across various applications, from developing new vaccines and sustainable chemicals to novel diagnostics and agricultural crop protection. Our goal is now to put Cradle’s software into the hands of a million scientists and empower them to build great products. Our Series B will make this next phase of growth possible, and we’re delighted to have the backing of IVP to help us scale.”

Alex Lim, General Partner at IVP, commented: “Biology is one of the domains where generative AI can have the biggest positive impact. Cradle is leading the way with its pioneering approach to protein design as a digital service. Given the costs associated with drug discovery or similar research fields, any efficiencies at the R&D stage will translate to both major financial returns for customers and significant real-world benefits for humanity. With impressive results delivered by Cradle’s platform just two years after launch, we see a bright future ahead for one of Europe’s - and the world’s - most consequential AI companies.”

The Series B funding will partly be used to expand Cradle’s wet lab and generate additional datasets to train Cradle’s models to address a growing array of challenges and modalities. Cradle will also expand its engineering team to improve its ML capabilities further to better generalise across tasks and handle more complex proteins.