In order to gain more insight into the differences in metrics obtained with spQAP and atom pairs, the relative metrics were calculated as the ratio of metric value obtained with spQAP to the value obtained with atom pairs minus 1. The ratio was calculated for each cross validation fold and visualized in figures 3 and 4. The quantum modification of atom pairs caused an average value of R 2 increase from 0.5% to 6% and the RMSE decreased about 0.5% to 5% depending on the predicted property. In the case of predicting pIC50, the average decrease of R 2 was 2% and the RMSE increased by about 3%. tors and RDKit fingerprints. The phenomenon may result from the imbalanced nature of the BBBP and ClinTox datasets. Although ROC AUC is widely used in evaluating classifiers, there are reports that it may be misleading when the dataset is imbalanced. To complement the results, an U Mann-Whithney test was applied to assess whether the results of metrics vary between spQAP and atom pairs. At the significance level of 0.05, the test indicated that the results are significantly different in the case of predicting logP, lipophilicity and melting point. This may indicate that the addition of quantum information to atom pairs is more effective in regression tasks.