Before revision:  We benchmark our model by showing that it outperforms recent graph pooling layers on molecular graph understanding and prediction tasks. We then demonstrate improved interpretability by identifying important molecular substructures and generating novel and valid molecules, with important applications in drug discovery and pharmacology .
After revision:  We benchmark our model by showing that it outperforms recent graph pooling layers on molecular graph understanding and highlight its potential benefits in drug design. Finally, we demonstrate LaPool's utility for the generation of valid and novel molecules by incorporating it into an adversarial autoencoder .