ArticlePatterns (New York, N.Y.)2022
Quantitative evaluation of explainable graph neural networks for molecular property prediction.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- Infrared Spectra Prediction for a Carbonyl Group Utilizing a Graph Network Approach.Precision chemistry · 2026Article
- Systematic validation of graph neural network explanations against adverse outcome pathway reactive centers for skin sensitization.Journal of cheminformatics · 2026Article
- DeepSnap: From Three-Dimensional Molecular Images to Quantitative Structure-Activity Predictions.International journal of molecular sciences · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Framework for evaluating explainable AI in antimicrobial drug discovery.Journal of cheminformatics · 2026Article
- Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization.Computational and structural biotechnology journal · 2026Article
- Explainable Artificial Intelligence: A Perspective on Drug Discovery.Pharmaceutics · 2025Review
- ACES-GNN: can graph neural network learn to explain activity cliffs?Digital discovery · 2025Article
- Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.Biomedical engineering online · 2025Review
- A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network.Frontiers in toxicology · 2025Article
- Bitter peptide prediction using graph neural networks.Journal of cheminformatics · 2024Article
- Cross-Modal Graph Contrastive Learning with Cellular Images.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.Briefings in bioinformatics · 2024Article
- Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX.Communications chemistry · 2024Article
- From intuition to AI: evolution of small molecule representations in drug discovery.Briefings in bioinformatics · 2023Article
- Explaining compound activity predictions with a substructure-aware loss for graph neural networks.Journal of cheminformatics · 2023Article
- Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking.Nature communications · 2023Article
- A Perspective on Explanations of Molecular Prediction Models.Journal of chemical theory and computation · 2023Review
- Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 reliable and informative answers for medicinal chemists in identifying the key substructures. Moreover, the identified substructures were shown to complement existing classical fingerprints to improve molecular property predictions, and the improvements increased with the growth of training data.
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