About
Cradle is a specialized AI platform designed to accelerate protein engineering for biopharma and industrial biotechnology R&D teams. Rather than relying solely on manual rational design or exhaustive experimental iteration, Cradle uses machine learning models—trained on both public biological databases and your proprietary wet lab data—to intelligently generate and rank protein candidates most likely to meet your desired objectives. At its core, Cradle enables multi-property co-optimization, allowing scientists to simultaneously target improvements across activity, binding affinity, thermostability, and expression levels—properties that often involve complex trade-offs. As experimental results are uploaded after each round, the models compound their learning, delivering increasingly accurate predictions and more successful candidates over time. The platform supports any protein type, from therapeutic antibodies and peptides to industrial enzymes and vaccine antigens. It provides a structured workflow for tracking design rounds, exploring multiple parallel design strategies, managing data, and generating actionable reports—all within a collaborative interface tailored for scientific teams. Cradle is trusted by leading organizations such as Novonesis and has been used in case studies ranging from engineering therapeutic peptides with a 50% success rate to improving vaccine protein thermostability by 2.5 °C in a single round. It is built for teams looking to move from incremental, slow-paced iteration to compounding progress—bringing novel bioproducts to market in quarters rather than years.
Key Features
- AI Protein Candidate Generation: Automatically generate diverse protein candidates optimized toward your specified property targets, exploring the design space far beyond what manual methods allow.
- Multi-Property Co-Optimization: Simultaneously optimize across activity, binding affinity, thermostability, expression levels, and more—resolving complex trade-offs in fewer experimental rounds.
- Iterative Learning from Wet Lab Data: Models improve with every round of uploaded experimental results, compounding accuracy over time and making each subsequent design cycle more efficient.
- Round & Project Management: Track multiple protein engineering programs end-to-end, from initial candidate generation through experimental validation, with clear visibility at every stage.
- Exploration of Parallel Design Strategies: Run multiple design strategies in parallel to reduce the risk of dead ends, strengthen IP portfolios, and increase the number of programs that reach their goals.
Use Cases
- Engineering therapeutic antibodies or peptides with high success rates under tight multi-property constraints for biopharma R&D teams
- Improving thermostability of vaccine antigens in fewer experimental rounds to accelerate vaccine development pipelines
- Optimizing industrial enzymes for performance, stability, and expression to reduce bioprocess development costs and timelines
- Running parallel protein design strategies across multiple programs simultaneously to expand IP portfolios and reduce pipeline risk
- Producing sustainable biosolutions—such as vaccine adjuvants—with improved properties while minimizing environmental impact
Pros
- Dramatically Accelerates Development: Teams using Cradle report 2–12× faster development timelines, enabling products to reach market in quarters rather than years.
- Learns from Your Proprietary Data: The platform's models are guided by your own experimental results, continuously improving predictions and making each round more productive.
- Handles Complex Multi-Property Trade-offs: Co-optimizing multiple protein properties simultaneously is a notoriously difficult problem; Cradle's AI resolves these trade-offs in an integrated, data-driven way.
- Proven with Top-Tier Biopharma: Trusted by leading organizations like Novonesis with documented case studies demonstrating real-world efficacy across therapeutic and industrial applications.
Cons
- Enterprise-Only Pricing: Cradle operates on a sales-led, enterprise pricing model, which may be cost-prohibitive or inaccessible for academic labs or early-stage startups.
- Requires Experimental Data to Maximize Value: While the platform can leverage public data initially, its iterative learning and compounding results depend on uploading proprietary wet lab data over multiple rounds.
- Narrowly Specialized Domain: Cradle is purpose-built for protein engineering and is not suited for broader drug discovery, genomics, or other areas of computational biology.
Frequently Asked Questions
What types of proteins can Cradle work with?
Cradle is built to work with any protein type, including therapeutic antibodies, peptides, industrial enzymes, and vaccine antigens, across any set of desired properties.
How does Cradle use experimental data?
After each wet lab round, scientists upload their experimental results to Cradle. The platform uses this data to retrain and refine its predictive models, improving the accuracy and success rate of each subsequent round of candidate generation.
How much faster can Cradle make protein development?
Teams using Cradle have reported 2–12× faster development timelines. Published case studies include achieving a 50% success rate on therapeutic peptides 5× faster than prior efforts and improving vaccine thermostability 7× faster than rational design.
Who is Cradle designed for?
Cradle is designed for professional scientists and R&D teams at biopharma and industrial biotech companies who are actively running wet lab protein engineering programs and want to accelerate them with AI.
What is CRADLE-1?
CRADLE-1 is Cradle's foundation protein engineering model, introduced as a significant advancement in their AI capabilities. A whitepaper describing its architecture and performance has been released by the company.
