loading . . . CholBindNet as an interpretable neural network for cholesterol-binding site classification Communications Chemistry, Published online: 29 May 2026; doi:10.1038/s42004-026-02064-wCholesterol is a key modulator of membrane protein structure and function, yet predicting cholesterol-binding sites remains challenging due to the relatively limited data on cholesterol-binding pockets compared to drug-like ligand binding sites. Here, the authors report CholBindNet, an interpretable, atom-based graph neural network that relies on a positive-unlabeled (PU) training strategy, demonstrating its superior performance over existing models and assessing strong, moderate, and weak cholesterol-binding sites in the PIEZO2 ion channel against all-atom molecular dynamics simulations. http://dlvr.it/TSn89r
