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Found 3 papers, 1 papers with code
Prototype-based Interpretable Graph Neural Networks
We then evaluate the explanations of the interpretable models by comparing them with post-hoc approaches and self-explainable models.
Molecule Generation from Input-Attributions over Graph Convolutional Networks
It is well known that Drug Design is often a costly process both in terms of time and economic effort.
Semi-Supervised GCN for learning Molecular Structure-Activity Relationships
Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups.
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