University of Missouri researchers have unveiled PSBench, the world’s largest publicly available database of protein structure models with quality assessment. The resource includes 1.4 million annotated protein structure predictions, each independently verified by expert scientists. This comprehensive benchmark is designed to improve the accuracy and reliability of artificial intelligence systems used to predict protein structures, a crucial step in advancing drug discovery and developing treatments for complex diseases such as Alzheimer’s and cancer. Proteins are fundamental to biological function, and their three-dimensional shapes determine how they interact with other molecules. Accurately predicting these structures is essential for understanding disease mechanisms and designing targeted therapies. While recent advances in AI—most notably DeepMind’s AlphaFold—have dramatically improved protein structure prediction, challenges remain in assessing the reliability of these models, especially when experimental data is limited. PSBench addresses this gap by offering a vast, high-quality dataset with detailed quality scores for each model. By providing a standardized benchmark, the database enables researchers to train and test AI algorithms more effectively, helping to identify which predictions are trustworthy and which require further validation. This can significantly reduce the time and cost associated with experimental validation. The database is expected to serve as a foundational tool for the scientific community, supporting efforts in structural biology, computational drug design, and AI development. With its scale and rigorous quality control, PSBench represents a major leap forward in the quest to decode the protein universe and unlock new avenues for medical innovation.