Our explainability framework relies on the recently proposed Img2Mol model. It consists of a convolutional neural network whose task is to map molecular graphical depictions to their CDDD embeddings. The CDDD space C = [-1, 1] 512 is constructed as the bottleneck layer of a Seq2Seq-autoencoder network trained to translate several million chemically-equivalent SMILES representations of molecules and defines a continuous molecular descriptor, which can be utilized as a powerful input for training downstream tasks. Figure depicts the structure of the Img2Mol encoder. Img2Mol is trained on over ten million unique canonical SMILES and establishes the new state-of-the-art performance in reconstruction accuracy. The training objective consists in minimizing the distance in CDDD space between the Img2Mol embeddings and the embeddings obtained through the encoder from . The reconstruction from CDDD to SMILES to evaluate the model's performance occurs through the pre-trained decoder from . For further details concerning the model architecture, as well as the training procedure and the model performance, we refer the reader to .