CradleNEW

Cradle is a specialized generative AI platform designed for protein engineering and optimization in the biotechnology sector. The software assists biologists and bioinformaticians in systematically modifying proteins, enzymes, and antibodies to enhance properties such as stability, solubility, binding affinity, and expression levels. Rather than relying solely on time- and cost-intensive wet lab experiments, Cradle uses machine learning algorithms to predict optimal sequence variants in silico.

The platform integrates advanced deep learning models with a user-friendly interface that does not require deep programming knowledge in Python. Researchers can upload existing protein sequences, define specific design targets, and generate recommendations for point mutations or recombined sequences. Predictions are backed by confidence scores, enabling laboratory teams to synthesize and test only the most promising variants.

By establishing a closed feedback loop between wet lab testing and AI, model precision improves with every iteration. Experimental assay results from the lab are fed back into Cradle, fine-tuning the AI models for the specific project. This significantly accelerates development cycles in therapeutic and industrial biotechnology while dramatically reducing the number of screening rounds required.

• AI-Powered Sequence Optimization — Generates targeted mutation recommendations to improve properties such as thermal stability, binding affinity, or expression rates. Predictions are driven by state-of-the-art biophysical algorithms and generative protein language models.
• Iterative Lab-to-Loop Learning — Allows seamless re-importing of wet lab assay data into the platform. The system learns from both successful and failed experimental outcomes to progressively improve accuracy in subsequent design rounds.
• Collaborative Bioinformatics Workspace — Provides interactive visualizations and tools designed for interdisciplinary teams of wet lab scientists and computational biologists. Projects and variant libraries can be centrally managed, analyzed, and exported.

Biotech and pharmaceutical companies, academic research institutes, and synthetic biology startups focused on protein engineering. The platform caters to both computational biologists and wet lab scientists.

A biotechnology firm aims to engineer a more thermostable industrial enzyme for a chemical manufacturing process. The research team uploads the wild-type sequence to Cradle and defines thermal stability as the primary design goal. Cradle rapidly calculates a library of 96 promising sequence variants. The laboratory synthesizes this targeted batch, tests them in assays, and feeds the performance data back into the platform. By the second iteration round, the team successfully identifies a variant meeting all operational requirements, saving months of random screening.

• Biotech and pharmaceutical companies, academic research institutes, and synthetic biology startups focused on protein engineering. The platform caters to both computational biologists and wet lab scientists.
• A biotechnology firm aims to engineer a more thermostable industrial enzyme for a chemical manufacturing process.
• AI-Powered Sequence Optimization: Generates targeted mutation recommendations to improve properties such as thermal stability, binding affinity, or expression rates. Predictions are driven by state-of-the-art biophysical algorithms and generative protein language models.
• Iterative Lab-to-Loop Learning: Allows seamless re-importing of wet lab assay data into the platform. The system learns from both successful and failed experimental outcomes to progressively improve accuracy in subsequent design rounds.
• Collaborative Bioinformatics Workspace: Provides interactive visualizations and tools designed for interdisciplinary teams of wet lab scientists and computational biologists. Projects and variant libraries can be centrally managed, analyzed, and exported.

Unsuitable for general text or image generation tasks outside of the life sciences. For users only seeking basic 3D protein structure visualization without sequence design and optimization, dedicated molecular viewers are more appropriate.

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