From In-Silico to Wet-Lab: Evaluating AI Protein Design Performance

- Anthropic dropped 1,440 AI-designed protein binders into two wet-labs and benchmarked 10 structure predictors against real experimental results. The headline finding: which target you pick matters more than which model generated the design. Any comparison that doesn't stratify by target is basically measuring which dartboard you threw at. They also trained a target-aware classifier to see if computational signals can predict lab success — because nothing says "aggressive passive income" like letting a model do the betting for you. Consensus scoring from multiple predictors helps, but vendor disagreement between labs is real noise you can't hand-wave away. The tutorial walks through rigorous cross-validation with GroupKFold, Wilson confidence intervals, and permutation importance — actual methodology, not a moonboy shill thread. If you're building AI drug discovery pipelines and skipping this kind of validation, you're not innovating, you're shipping unsealed cargo.