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

MarkTechPost · 1 month ago · 33

The tutorial evaluates Anthropic’s claude-protein-binder-design dataset by comparing in-silico structure predictor outputs against wet-lab miniprotein binder assay results. It analyzes 1,440 AI-designed miniprotein binders tested across 16 targets, including both computational predictions and measurements from two independent labs. It shows how target effects dominate, that consensus across multiple predictors can improve ranking and estimate experimental hit rates under limited testing budgets, and that disagreement and structure-prediction signals can be used to build a target-aware classifier for experimental success.