Common modeling strategies involve fully connected networks from pre-computed molecular fingerprints or latent representations and, when enough training data is available, end-to-end training with graph convolutional networks (GCNs) . Explanations for these types of models can take the form of atomic attributions (particularly for GCNs ), or feature importance using packages such as SHAP . Many of our in-house models are built upon the CDDD embedding space , which poses a challenge for explainability. The CDDD space is the bottleneck layer of a pre-trained autoencoder translating between different SMILES representations of molecules. One approach to explainability consists of assigning the attributions to the original SMILES, i.e., tracing back gradients through the pre-trained encoder. However, the interpretation and visualization of attributions for string characters is challenging . Additionally, the validity of the use of gradients for discrete character inputs can also be questioned .