Opportunities and challenges in protein structure prediction

Abstract Deep learning methods, particularly exemplified by AlphaFold2, have revolutionized the field of protein structure prediction—an achievement recognized by the 2024 Nobel Prize in Chemistry awarded to its core developers. Despite this remarkable achievement, the broader protein folding problem is far from solved. Key challenges—each representing opportunities for future breakthroughs—include single‐sequence structure prediction, modeling protein dynamics, accurately predicting multimeric complexes, and effectively incorporating experimental constraints. Here we review recent progress in these key frontiers and share our perspective on future directions.