A selection campaign is a measurement, not just a ranking.
Models trained on existing sequence and structure data can propose plausible molecules. Metric generates direct evidence from the exact target, construct, and conditions of a program, then models how the experiment shaped the observations.
The output is a design landscape.
Metric models molecular behavior together with the experiment that produced the data. The resulting interaction model represents a biophysical design landscape for the target and conditions that matter to the program.
That landscape guides molecular AI beyond the observed candidates.
We separate molecular behavior from the experiment that measured it.
Molecular behavior
How sequence shapes the required interaction profile.
Experimental process
How the assay and sequencing process shape what is observed.
What the data can resolve
Which molecular effects are supported and where uncertainty remains.
Where the evidence ends
Which regions of sequence space are supported by the campaign.
Design is constrained by what the program has measured.
Every design traces back to evidence the campaign actually produced, not to prior assumptions alone.
By learning the interaction before individual validation, the platform is designed to reduce the number of builds and experimental cycles needed to reach a qualified candidate panel.