While traditional scoring functions have been successful in scoring protein-ligand interactions using hand-crafted 3D features, recent advances in deep learning provide the ability to predict scores directly from the 3D structure of the protein-ligand conformation. Deep learning scoring functions utilize multiple layers of learned weights to learn complex functions directly from protein and ligand poses to evaluate the quality of binding. Convolutional neural networks (CNNs) are widely used in computer vision, particularly for grid-structured data, such as images. They can capture both low-level and high-level spatial patterns or features by performing convolutions across the input. Recent studies have demonstrated the considerable potential of CNNs in molecular docking scoring, exemplified by AtomNet 4 and PotentialNet. Notably, GNINA has proposed several effective CNN architectures for scoring binding poses and predicting affinity. More recent work has proposed graph neural networks (GNNs) for both pose scoring and pose sampling. Both CNNs and GNNs show marked improvement in docking over traditional scoring methods; however, due to heavy computation, they require the use of a GPU for reduced docking time.