University of Missouri researchers released PSBench, the world’s largest annotated collection of protein structure models—about 1.4 million entries—with expert quality assessments to benchmark and improve AI‑based structure prediction. The resource is intended to help groups build better model‑quality estimators and increase confidence in AI outputs used for drug discovery. PSBench pairs models with independent expert validation, enabling training of downstream metrics that distinguish reliable from erroneous predictions. The dataset should accelerate development of quality‑aware AI components in structural biology pipelines and help reduce false leads in target-based programs. Biotech R&D teams using structure prediction for hit identification or lead optimization can integrate PSBench to validate model confidence and refine computational triage, reducing costly experimental follow-up on low‑quality predictions.