The "atom attribution from finger prints"-method developed by Riniker and Landrum computes the contribution of a given (non-hydrogen) atom by removing all bits from the fingerprint for which the corresponding fragments contain the atom (cf Figure ). The ML predicted value using this new fingerprint is subtracted from the value predicted with the unmodified fingerprint and the difference is attributed to that atom. This results in a vector of atom attributions that we want to compare to the corresponding atom contributionsvector (the ground truth vector) from the Crippen logP model. While the ground truth vector sums up to the ground truth logP value, the atom attribution vector does not sum up to the ML-predicted logP value. The vector elements are in fact very different in magnitude so a simple difference is not instructive. Sanchez-Lengeling et al. used Kendall's tau (rank correlation) while Henderson et al. used Pearson's r to quantify agreement. We choose to compute the dot product of the normalised atom attribution and ground truth vectors, which ranges from -1 to 1 and where 1 corresponds to a perfect agreement. This overlap compares the distribution and relative importance of positive and negative contributions within the molecule, but it does not report directly on the contribution of each atom to the logP value. Thus, when comparing the vectors visually we re-scale the attribution vector so that it sums to the predicted logP value and depict the magnitude of these contributions as a contour map, while the color intensity corresponds to a "normalised" vector where the largest magnitude contribution is 1 (this vector is very similar to the normalised vectors used to compute the overlap, but gives better visual comparison). We compare the overlap to Pearson's r below.