A benchmark built from 50,000 ChEMBL molecules shows that current explainers for graph neural networks often fail to identify the chemical substructures that define the prediction.
Evaluating explainability for graph neural networks.Scientific Data, 10(144), 2023
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B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
A benchmark built from 50,000 ChEMBL molecules shows that current explainers for graph neural networks often fail to identify the chemical substructures that define the prediction.