Quantitative evaluation of explainable graph neural networks for molecular property prediction.

ABSTRACT: 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 methods are limited to qualitative analyses without quantitative assessments from the real-world datasets due to a lack of ground truths. In this study, we have established five XAI-specific molecular property benchmarks, including two synthetic and three experimental datasets. Through the datasets, we quantitatively assessed six XAI methods on four GNN models and made comparisons with seven medicinal chemists of different experience levels. The results demonstrated that XAI methods could deliver re

SUBMITTER: Rao J 

PROVIDER: S-EPMC9782255 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature