Cradle

Simultaneously optimizes multiple protein properties (fold stability, expression levels, activity) using deep learning models to find designs that balance competing engineering objectives without requiring extensive wet lab screening.

Predicts and optimizes protein thermodynamic stability and folding properties using AI models trained on protein structure data, enabling design of more robust engineered proteins.

Predicts and optimizes codon usage, secondary structure, and sequence features that influence protein expression yields in host cells, enabling design of highly-expressed engineered proteins.

Predicts how sequence mutations affect protein catalytic activity, binding affinity, or other functional properties using deep learning models trained on functional protein data.

Generates multiple candidate protein sequences with predicted improvements across specified properties, creating a design library for experimental validation without exhaustive computational screening.

Integrates computational protein design results into existing biotech laboratory information management systems and experimental workflows, enabling seamless handoff from AI design to wet lab validation.

Analyzes protein engineering projects to estimate how many fewer experimental iterations will be needed by using AI-guided design versus traditional high-throughput screening, helping teams quantify R&D cost and timeline savings.

Allows users to define and enforce constraints on protein designs such as sequence identity to parent protein, avoidance of specific mutations, or maintenance of critical residues, ensuring optimized designs remain practical and safe.