Although different explainability approaches qualitatively agree with chemical intuition, some differences between the explanations are observed for quantitative structure-activity relationships (QSAR) and GNNs . The disagreement problem occurs when explanations for the same chemical property generated by different methods are inconsistent , indicating uncertainty in the generated explanations of machine-learned models. Furthermore, inaccurate attribution can lead to unwarranted trust in models . Reasons for the disagreement at the atomistic level include incorporating alchemical steps with limited chemical validity , noise in the gradient estimation , and bottlenecks in GNNs . Currently, the best-performing method for different explanation tasks varies, and no single method performs well across various tasks .