Predictive modeling

Predictive modeling is the lever for avoiding expensive wet-lab iterations. Before sequences are synthesized, cloned and expressed, we simulate the relevant molecular behaviour in silico and filter the design space down to what actually has a chance of working.

Our toolkit combines molecular dynamics, energetic evaluation of binding and interface states, conformational sampling and ML-based property predictors into one consistent picture of each candidate.

The output is concrete: expected binding affinity, conformational dynamics, stability landscapes and risks — not isolated scores, but profiles set against your real target application.

What you get back is a decision-ready list: which candidates move into synthesis and wet lab, which are parked, and which can be eliminated based on clear weaknesses.