Abstract: While there is a great deal of interest in methods aimed at explaining machine learning predictions of chemical properties, it is difficult to quantitatively benchmark such methods, especially for regression tasks. We show that the Crippen logP model provides an excellent benchmark for atomic attribution/heatmap approaches, especially if the ground truth heatmaps can be adjusted to reflect the molecular representation. I give some examples of how this benchmark can be used to get a better understanding of ML models work and how it can be used to determine which techniques for generating XAI heatmaps works the best.
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