If an AI model gives your team hundreds of promising protein designs, how quickly can you work out which ones deserve more attention?

As I continue learning about protein design and biophysical analysis, I keep coming back to this question. Generating candidates is exciting. Building a practical way to evaluate them is what makes those predictions useful.

The prediction is where testing begins

In its October 20, 2026 report , Anthropic described a campaign that generated 318,484 designs , selected 211 for experimental testing , and identified 17 that met its binder criteria .

Adaptyv Bio and Twist Bioscience used surface plasmon resonance, or SPR , to investigate binding in different protein-production formats. The criteria included reproducibility, an affinity threshold and no detected binding to an unrelated control.…