Moreover, the representation quality of proteins and ligands-essential components of PCMs-often receives insufficient scrutiny. While various descriptors and embeddings are employed to capture the complex nature of protein-ligand interactions, their effectiveness in truly enhancing model performance remains underexplored. For ligands, common representations include one-hot encoding, SMILES2vec, Morgan Fingerprints, and chemical graphs . Proteins are often represented by methods such as one-hot encodings, learned embeddings from structure prediction models, molecular graphs, or 3D images, where each channel highlights a different atom type in the binding pocket . Furthermore, protein-ligand interactions might be depicted through voxelized grids or interaction graphs . Examples of PCMs utilizing these representations are RFScore 9 , DeepDTA 7 , GraphDTA 25 , AtomNet , and 3D-KINEssence , which demonstrate the diverse approaches in modeling but also highlight the variability in their predictive success.