Graph neural networks (GNNs) were used using a GrASP web server that utilizes a pretrained GNN model to predict the most probable binding site for the fibril . In contrast with traditional neural networks that primarily handle structured data, GNNs capture the relationships and interactions between nodes in a graph. About binding, the molecules are represented as graphs where atoms are the nodes, and the bonds are the edges. The attention mechanism then calculated and highlighted the most likely druggable sites (figures 2c and 2d). The P2Rank web server further confirmed the results (figures 2a and 2b), where machine learning was utilized to predict and rank the ligand binding sites on the fibril. The GNN and ML results were used to validate the docking results.