Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening
Michela Proietti,
Alessio Ragno,
Biagio La Rosa
et al.
Abstract:Molecular property prediction is a fundamental task in the field of drug discovery. Several works use graph neural networks to leverage molecular graph representations. Although they have been successfully applied in a variety of applications, their decision process is not transparent. In this work, we adapt concept whitening to graph neural networks. This approach is an explainability method used to build an inherently interpretable model, which allows identifying the concepts and consequently the structural … Show more
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