To sum up, the "atom attribution from finger prints"-method developed by Riniker and Landrum, when used with a RF model, gives atomic attributions that are in reasonable agreement with the atomic contributions of the Crippen logP model for most molecules. The agreement is increased significantly when the atomic contributions are adjusted to match the fact that the molecular representation is fragment-based rather than atom-based (the FPA ground truth vector). Molecules where the atomic attribution differs significantly from the ground truth tend to have slightly larger errors on average, but the correlation factor is near zero when considering individual molecules. One question is whether cases such as 3 with low errors and wrong attribution is due to overfitting. The fact that the ML-model has learned non-additive correlations between structurally distant FP fragments is at odds with the additive and local nature of the ground truth and thus consistent with overfitting. On the other hand, the problem (as measured by the percentage of molecules with "sign problems") is most severe for the largest training set where one would expect overfitting to be less important. A likely explanation is that the use of a non-additive representation (binary FPs) to model an additive property, combined with bit collision, results in an intrinsically overfit model. For example, only two FP fragments make significant contributions to the predicted logP of n-butane: CH3and CH2-CH3-CH2-. The fragment that indicates that there are two of each of these fragments in n-butane (CH3-CH2-CH2-CH3) is not in the training set so that bit position is used for another fragment not contained in n-butane (bit collision). Even if n-butane was contained in the training set, the corresponding fragment would only appear once and the model would learn that this bit position is far more likely to report on some other non-pertinent fragment. For example, there are only 24 instances where bit 94 corresponds to the fragment shown in Figure , compared to 6050 instances for the most popular fragment, so this bit cannot be used by the model to predict logP for molecule 1. Thus, rather than making use of large fragments to help estimate the number and proximity of smaller fragments, the model is forced to learn correlations between smaller fragments. As a result, spurious correlations between fragments such as those seen for molecules 2-4 can occur because the representation does not effectively report on whether, for example, the fragments shown in Figure are in the same ring or not. A corollary of this hypothesis is that the Crippen logP values represent a challenging benchmark for the ECFP4/RF model and, hence, the atomic attribution method.