Explaining predictions on molecules is difficult because 3D and especially 2D visualizations of molecules that experts are familiar with are abstract models themselves and do not convey all of a molecule's complexity. One early example of explanations for chemical data used SHAP to obtain explanations of activity predictions . In Figure this method was reproduced for a kinase inhibitor example with an XGBoost model trained on the kinase inhibitor dataset. Like Myerson values, the SHAP values are a numerical value, in this case denoting how much a feature shifts the prediction from the expected value (based on the dataset). Since SHAP does not work for arbitrary sized input data, the molecule has to be a fixed sized representation, the most common being extended-connectivity fingerprints (ECFPs).