Abstract The field of explainable artificial intelligence (AI) has garnered significant research interest. In particular, “feature attribution” in the field of chemistry has been focused upon. However, studies on comparisons of the relationship between AI- and human-based feature attributions when predicting the same outcome are scarce. Thus, this study aimed to investigate this relationship by comparing machine-learning-based feature attributions (graph neural networks and integrated gradients) with those of chemists (Hansch–Fujita method) considering the case of predicting water–solubility. The AI-based attributions were found to be similar to those of chemists despite their distinct origins.

Feature attributions for water–solubility predictions obtained via artificial intelligence methods and chemists