To further validate the performance of ABP compared to sum pooling for each algorithm (i.e. SchNet and DimeNet++ in Table ), we performed Wilcoxon signed-rank tests as the data is not normally distributed according to the normaltest function available in scipy: p = 3.58 × 10 When compared to OWA pooling for HOMO energy prediction (Table ), attention-based pooling matches or even slightly outperforms OWA depending on the ABP configuration, despite not leveraging precomputed orbital coefficients. This can be observed both in terms of RMSE (the metric chosen by Chen Here, we also study LUMO energy prediction which is not considered by Chen et al., but is available in the OE62 dataset. We find that OWA offers a smaller improvement with respect to average pooling on LUMO energy prediction (8.04%) compared to HOMO energy (21.02%), as given by the MAE, with similar trends for the RMSE (Table ). Furthermore, whereas for HOMO the attention-based pooling offered a small but noticeable improvement compared to OWA, for LUMO we observe a more significant improvement of 21.16% for ABP.