For graphs such as molecules, however, no direct visualizations have been implemented. SHAP has been used for the explanation of activity predictions where molecules were represented as Morgan fingerprints , to rank nondescript bits. The authors were able to map bits back to the molecular structure to identify key-functional groups for their predictions. Morgan fingerprints are widely used molecular representations that have proven to be valuable for virtual screening and as inputs for machine learning, as their fixed length makes mathematical operations on them trivial . However, drawbacks to these fingerprints are the possibility of bit collisions, where multiple distinct structures activate the same bit in the fingerprint. It is also possible that a certain substructure occurs more than once in a molecule, but they are all represented by the same bit, making it impossible to deconvolute which substructures are important. Morgan fingerprints are generated from the molecular graph, and the rising popularity of graph neural networks (GNNs), that allow models to directly learn on graphs, obviate the need to generate these fingerprints. GNN input consists of a combination of multiple matrices of variable size, instead of a single vector or single matrix of fixed size, as would be the case for other machine learning models . This makes the explanation of GNN features challenging, even for model agnostic XAI methods, such as SHAP. Despite this, graphs offer the opportunity to directly correlate the structure of the graph with a prediction, a desirable property for drug discovery workflows.