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- Title
Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening.
- Authors
Proietti, Michela; Ragno, Alessio; Rosa, Biagio La; Ragno, Rino; Capobianco, Roberto
- 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 parts of the molecules that are relevant for the output predictions. We test popular models on several benchmark datasets from MoleculeNet. Starting from previous work, we identify the most significant molecular properties to be used as concepts to perform classification. We show that the addition of concept whitening layers brings an improvement in both classification performance and interpretability. Finally, we provide several structural and conceptual explanations for the predictions.
- Subjects
GRAPH neural networks; DRUG discovery; ARTIFICIAL intelligence; REPRESENTATIONS of graphs; MOLECULAR graphs
- Publication
Machine Learning, 2024, Vol 113, Issue 4, p2013
- ISSN
0885-6125
- Publication type
Article
- DOI
10.1007/s10994-023-06369-y