Benchmarking Protein–Ligand Cofolding Models: Correct Ligand Placement Is Decoupled From Accurate Protein Conformation

Abstract

AI-driven cofolding models have become central tools for predicting protein–ligand complexes, yet it remains unclear whether the resulting structures recover the correct protein conformation or merely place the ligand plausibly within an otherwise mispredicted fold. Here, we benchmark four state-of-the-art cofolding models, AlphaFold3, RoseTTAFold3, Boltz-2, and Chai-1, and show that correct ligand placement is systematically decoupled from accurate prediction of the surrounding protein conformation. As test systems, we use kinases and Class A G protein-coupled receptors (GPCRs), families whose pharmacology depends on well-defined conformational states. To enable a prospective assessment, we evaluate complexes released after the training cutoffs of all tested models, together with one earlier nucleotide-bound structure as a control. We find that state-defining structural features, including kinase activation-loop, DFG/DYG geometries, and GPCR intracellular conformations such as ICL3, are frequently mispredicted even when ligand root-mean-square deviation (RMSD) is low. Cofolding adaptively remodels the orthosteric pocket around the bound ligand, but this local adaptation rarely propagates to the correct global conformation. Incorporating state-annotated templates and filtered multiple sequence alignments (MSAs) improves conformational recovery in some cases but has little effect in others, and model confidence metrics correlate with small-molecule placement but cannot report whether the local pocket geometry was recovered. Furthermore, while orthosteric ligand placement is generally reliable, the single allosteric complex in our set is misplaced by all models, even under explicit guidance. These results identify conformational decoupling as a fundamental limitation of current cofolding approaches across the systems evaluated, with direct implications for state-selective and structure-based drug design.