As mentioned in the introduction, Sanchez-Lengeling et al. have tested graph-based atom attribution methods by fitting solubility data and then comparing the atomic attributions to the atomic contributions from the Crippen logP model -an approach that has also been used by Henderson et al. . Both used correlation coefficients to compare the vectors for individual molecules: Sanchez-Lengeling et al. used Kendall's tau (rank correlation) while Henderson et al. used Pearson's r. In general we find that there is a good correlation between Pearson's r and the overlap. For example, for the 150k training set the average Pearson's r values for the ground truth and FPA ground truth are 0.46 and 0.74, which are in reasonable agreement with the corresponding average overlaps of 0.54 and 0.75 (Table ). However, comparing R and overlap values for the four molecules in Figure the R value occasionally overestimates the agreement with the ground truth vector. For example, for molecules 3 and 4 the R values indicate significantly above-average agreements with the ground truth vector, while the overlaps indicate slightly below-average agreements. ), which roughly corresponds to the size of the solubility dataset used in these studies. As pointed out in both studies, these average r values are relatively low and our study suggests that perhaps they could be improved somewhat by fitting the model to the Crippen logP values. However, a more important factor could be that the ground truth attribution must be (somehow) adapted to better reflect the molecular representation used in these graph-based models.