Testing How Well AI Designs Real Proteins

Designing proteins with AI sounds great, but it often fails in the real world. We took a dataset of over a thousand AI-designed protein binders to see how well they hold up. We compared ten popular prediction models to find out which ones actually produce results.

Our tests show that success depends on a few key factors. We looked at how target identity and consensus scoring influence the final output. This helps you understand why some designs succeed while others fail during lab experiments.

Read our full guide to improve your own protein design workflow. We provide clear steps to help you validate your data before you start testing in the lab. These tips will save you time and help you get better results.