Machine learning models

We deploy modern ML and AI architectures trained on large protein sequence and structure datasets — from protein language models and structure prediction networks to generative models for de novo design.

We treat these models as building blocks, not black boxes. Every prediction is checked against structural and biochemical knowledge, cross-validated with physics-based methods, and put into a context that supports real design decisions.

Typical applications include predicting stability, binding and expression behaviour, transferring function between related scaffolds, and generating de novo sequences for defined target profiles.

The result is a workflow in which machine learning dramatically expands the addressable design space, without you losing control over the final output.