Supercharge your protein.
Physics and energy-based ML models built to equip your protein-protein interactions with the first principles of biology.
Biology has an AI problem.
Antibodies are among the most precise biologics we have, and they can reach diseases that other modalities can’t. For the first time, we also have the computational tools to understand why an antibody binds the way it does, before the first experiment is run.
But turning a validated target into a preclinical-ready antibody is still slow, expensive, and driven largely by trial and error. Most programs that fail, fail late, after months of wet-lab iteration, for reasons that were predictable from first principles all along.
From target brief to validated results.
Scope & Intake.
In Silico Prediction.
Wet-Lab Validation.
Review with Team.
Scale & Expand.
Engineering antibodies for speed & success.
De novo & optimisation design
Generate or optimise binders, scored by predicted binding energy from the first design round.
Epitope mapping
Resolve where and how an antibody engages its target, at residue-level resolution.
Binding affinity prediction
Rank candidates by predicted binding energy, decomposed into its contributing physical terms.
Docking
Model antibody–antigen complexes to evaluate binding poses and interface quality.
High-throughput screening
Run the full pipeline across a large candidate pool at once, for comparable batch-level results.