Another representation was based on Medu-saNet and allows for encoding the three dimensional shape and orientation of a ligand in the active site. For this representation we split the binding site into a grid of 2 Å voxels and counted the number of ligand atoms of each chemical element in each voxel resulting in a sparse 4-dimensional tensor. Unlike in the original MedusaNet paper , which dealt with convolutional neural networks, we used a one dimensional representation of the tensor, as we work with linear layers instead. We refer to this representation as atom-hot, as it is similar to one-hot encoding used to label training data for classifiers in machine learning, except for multiple atoms being able to occupy the same voxel. Additionally a modified version, called atom-hot-surf, was probed. This representation only considered voxels on the van der Waals surface of the binding pocket.