Following on AtomNet, several works have attempted to apply 3D convolutional neural networks (3D CNNs) by extracting chemical information of each protein-ligand complex from 3D grids. However, these methods lack topological information of structures and are sensitive to the rotation of complex structures. In contrast to grid based methods, biomolecules can be treated as graphs and Graph Neural Networks (GNNs) can directly take graph as input. Unsurprisingly, various GNNbased scoring functions were reported, such as PotentialNet , graphDelta , SIGN etc.