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Company description
Cradle is an AI-powered protein design and optimization platform built for biopharma and industrial biotechnology R&D teams. By leveraging machine learning trained on a team's own experimental data, Cradle enables scientists to generate optimized protein candidates and improve their properties — delivering more breakthroughs in fewer wet lab experiments. The platform is trusted by leading biopharma and industrial bio organizations, including teams at Corteva, and is purpose-built to compress development timelines from years to quarters.
Cradle's approach centers on a continuous design-validate loop: researchers design candidates in the platform, validate them in their own lab or with a CRO, and feed results back into Cradle so its models continuously improve. This compounding learning cycle means that every round of data makes the AI smarter, resulting in increasingly performant variants with each iteration. Teams using Cradle report 2–12× faster development timelines across their programs.
Core Platform Workflow
- Import and analyze data: Upload wet lab data from screening or optimization experiments, or define goals and assays from scratch. Cradle automatically analyzes experimental data quality and provides guidance on improving workflows and assays to reach optimal sequences faster.
- Generate candidates: At the click of a button, generate new optimized protein candidates by choosing a design strategy, setting sequence- or lab-based constraints, and incorporating existing protein knowledge — no computer science expertise required.
- Multi-property optimization: Cradle learns the complex interactions between all protein properties simultaneously, producing candidates that balance improved performance across activity, binding affinity, stability, expression, specificity, and more.
- Full-plate optimization: Downloaded sequences represent a carefully curated set selected from millions of in silico designs to maximize experimental success rates.
- Generation Reports: Review predictive performance scores for entire plates, explore specific sequences and mutations in 3D, and decide which candidates to bring to the lab with full visibility into expected performance.
- Round status tracking: Monitor lab progress directly in Cradle with live metrics per protein property as new assay data is returned from testing.
Supported Protein Types and Applications
- Antibodies: Engineer mature, developable binders while avoiding unwanted immunogenicity.
- Enzymes: Accelerate specific catalytic conversions, even under challenging conditions.
- Vaccines: Stabilize antigens and reach therapeutic goals faster — demonstrated by improving Staphylococcus aureus vaccine thermostability by 2.5°C in a single round, 7× faster than rational design.
- Peptides: Achieve desired efficacy while ensuring stability, with demonstrated 50% success rates under tight multi-property constraints, 5× faster than prior efforts.
- Applicable across therapeutics, agricultural solutions, food ingredients, and other life sciences verticals — if a property can be measured, it can be optimized with Cradle.
Security, Privacy, and Compliance
- Privacy-first AI: Experimental data is used exclusively to train an organization's own custom models and is never used to train models for other customers.
- Data ownership: Users retain full intellectual property ownership of their sequences and data. Cradle operates on a software subscription fee with no royalties.
- SOC 2 compliant: Cradle maintains full SOC 2 compliance with active monitoring and rigorous security protocols, protected by bank-grade security.
- Single Sign-On (SSO): Secure and simple account management with SSO support for Google and Microsoft.
- Custom predictors: Support for internally developed predictors is available in beta.
Cradle also publishes research supporting its technology, including the CRADLE-1 whitepaper, and provides case studies demonstrating real-world impact — such as using AI to produce vaccine adjuvants without deforestation — underscoring its commitment to both scientific advancement and responsible innovation.
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