Developing accurate computational scoring functions (SFs) for assessing protein-ligand binding affinity is an ongoing challenge, with approaches ranging from molecular fingerprints to docking. These approaches include fingerprint and atom-pair expert encodings (PLEC , LUNA and ECIF and docking-based SFs (Glide , RF-score , NN-score ). In contrast, new deep-learning methods are based on graph encoders. The application of graph encoders to cheminformatics tasks shows promise at improving existing scoring functions. Early research using deep learning models as SFs includes TopologyNet and several traditional convolutional models that use voxel-based representations of ligand-receptor complexes as inputs. The following paragraphs discuss several early applications using MPNNs to learn SFs. Our approach to generating ligand-receptor graphs resembles PLEC's implementation of proximity-based implicit graphs during fingerprint (FP) generation. Beyond this, works like ours focus on directly applying message-passing neural networks to the ligand-receptor graphs.