Quantitative evaluation of explainable graph neural networks for molecular property prediction

Graph neural networks (GNNs) have received increasing attention because of their expressive power on topological data, but they are still criticized for their lack of interpretability. To interpret GNN models, explainable artificial intelligence (XAI) methods have been developed. However, these meth...

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Detalles Bibliográficos
Autores principales: Rao, Jiahua, Zheng, Shuangjia, Lu, Yutong, Yang, Yuedong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782255/
https://www.ncbi.nlm.nih.gov/pubmed/36569553
http://dx.doi.org/10.1016/j.patter.2022.100628

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782255/
https://www.ncbi.nlm.nih.gov/pubmed/36569553
http://dx.doi.org/10.1016/j.patter.2022.100628

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