eLife Assessment
This study addresses an important gap in drug discovery by delivering a rigorous, large-scale evaluation of widely used co-folding methods for predicting ligand-bound protein complexes and virtual screening. A key strength is the comprehensive benchmarking framework, which leverages structures and chemical compounds that were absent from the AI models training set, thereby providing particularly compelling and unbiased evidence of co-folding performance. The findings clearly delineate the complementary roles of deep learning-based co-folding and physics-based docking, offering practical guidance for their rational integration into drug discovery workflows. Overall, the conclusions are well supported by thorough analyses across a representative set of cases.