ACER: Towards Generalizable Protein-ligand Co-folding

Predicting protein–ligand complex structures is a central challenge in drug discovery. While recent co-folding models such as AlphaFold-3 achieve accurate structure prediction, they fail to generalize to underexplored binding interfaces -- systematically misplacing ligands, particularly for allosteric or structurally novel targets. To address this gap, we present \textbf{ACER} (\textbf{A}daptive \textbf{C}o-folding via pocket \textbf{E}xploration and pose \textbf{R}anking), a training-free framework that (a) enables co-folding models to systematically explore alternative binding pockets, and (b) leverages the discovered pockets to increase pose accuracy. Our method enables the efficient discovery of non-prevalent pockets without prior expert knowledge. ACER improves pocket discovery and pose accuracy on allosteric targets and structurally novel complexes, successfully modeling binding interfaces that are under-represented or absent from the training set. Our results demonstrate how improved sampling dynamics enhance the generalisability of co-folding models without retraining.