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<p>Hierarchical pooling is a promising mechanism to enhance graph neural networks (GNNs) by enabling multi-scale representation learning. Rationalization of hierarchical GNN predictions remains an underexplored area. In this work, we investigate the impact of hierarchical pooling on GNNs for molecular property prediction. We designed architectural variants integrating pharmacophore features with pooling GNNs at different levels. GNN models with pharmacophore-based graph reduction or hierarchical pooling achieved comparable compound classification performance. Explainable artificial intelligence (XAI) methods were applied to compare feature importance and substructure attribution for the different model architectures. Qualitative and quantitative analyses of the resulting explanations demonstrated that the GNN variants had different internal learning characteristics. GNN models based on reduced graphs matched the prediction accuracy of models based on complete graph representations following different variant-dependent learning strategies.</p>

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Explainable artificial intelligence reveals divergent learning in pharmacophore-based hierarchical pooling graph neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Maria Julia Teja Urrutia,
  • Andrea Mastropietro,
  • Jürgen Bajorath

摘要

Hierarchical pooling is a promising mechanism to enhance graph neural networks (GNNs) by enabling multi-scale representation learning. Rationalization of hierarchical GNN predictions remains an underexplored area. In this work, we investigate the impact of hierarchical pooling on GNNs for molecular property prediction. We designed architectural variants integrating pharmacophore features with pooling GNNs at different levels. GNN models with pharmacophore-based graph reduction or hierarchical pooling achieved comparable compound classification performance. Explainable artificial intelligence (XAI) methods were applied to compare feature importance and substructure attribution for the different model architectures. Qualitative and quantitative analyses of the resulting explanations demonstrated that the GNN variants had different internal learning characteristics. GNN models based on reduced graphs matched the prediction accuracy of models based on complete graph representations following different variant-dependent learning strategies.