The lipophilicity dataset was taken from the MoleculeNet datasets . Two inhouse graph convolutional models were used to predict logD of the compounds from the dataset. Here, LogD is the logarithm of the partition coefficient of a compound between octanol and water, taking into account the charge state of the compound at a physiologically relevant pH. The first model is hereafter referred to as the "base model". The second model, here identified as "XAI model", was designed to be more interpretable by adding constraints during training . The dataset of 4200 compounds was uploaded to CIME. It contains the measured lipophilicity, the logD predictions by the two models, the models' latent space representations and atom contributions for both predictions. The Class Attribution Maps (CAM) methodology was adapted to graph neural networks to obtain the atom contributions for the two models.