Figure : Heatmap examples for four molecules . The first column is the heatmap from the ML model trained on 150k molecules. The second column is the ground truth heatmap from the atomic contributions of the Crippen model and the third column is the FP adapted ground truth heatmap (see text). ML contributions are scaled so they sum to ML prediction, AF contributions are scaled to sum logP. Crippen (atomic) contributions sum to logP by nature. *While the predicted logP is positive, summing the atomic contributions results in a negative number. Instead of scaling to logP pred , the atomic contributions are scaled to sum to -logP pred repeat this process for all the other atoms to produce a "finger print-adapted" (FPA) ground truth vector. A plot of this vector is shown in Figure and shows a near perfect agreement with the ML attribution vector, with an overlap of 0.99. Thus, the discrepancy between the attribution and ground truth vectors observed for this molecule is due to the way the attributions are computed and not a deficiency in the ML model itself. Using FPA ground truth vectors the average overlap for the test set increases significantly from 0.54 to 0.75, suggesting that this is the case for most molecules, although the average null-model overlap also increases, to 0.61 (Figure ).