Recent work in graph neural networks (GNNs) has lead to improvements in molecular activity and property prediction tasks. However, GNNs lack interpretability as they fail to capture the relative importance of various molecular substructures due to the absence of efficient intermediate pooling steps for sparse graphs . To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable  graph pooling method that takes into account the node features and graph structure to improve molecular understanding. Inspired by theories in graph signal processing, LaPool performs a feature-driven hierarchical segmentation of molecules by selecting a set of centroid nodes from a graph as cluster representatives. It then learns a sparse assignment of remaining nodes into these clusters using an attention mechanism. 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 .
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative importance of interactions between molecular substructures, in part due to the absence of efficient intermediate pooling steps . To address these issues , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable hierarchical graph pooling method that takes into account both node features and graph structure to improve molecular representation. We benchmark LaPool on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs. Interestingly, LaPool also remains competitive on non-molecular tasks. Both quantitative and qualitative assessments are done to demonstrate LaPool's improved interpretability 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 .
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lead
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However, GNNs lack interpretability as they
Unfortunately, GNNs often
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various molecular substructures
interactions between molecular substructures, in part
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for sparse graphs
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this issue
these issues
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hierarchical
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the
both
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understanding. Inspired by theories in graph signal processing, LaPool performs a feature-driven hierarchical segmentation of molecules by selecting a set of centroid nodes from a graph as cluster representatives. It then learns a sparse assignment of remaining nodes into these clusters using an attention mechanism. We benchmark our model by showing that it outperforms recent graph pooling layers
representation. We benchmark LaPool
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understanding
prediction and understanding tasks and show that it outperforms recent GNNs. Interestingly, LaPool also remains competitive on non-molecular tasks. Both quantitative and qualitative assessments are done to demonstrate LaPool's improved interpretability
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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
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