ArticleJournal of cheminformatics2023
Explaining compound activity predictions with a substructure-aware loss for graph neural networks.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- 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
- Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization.Computational and structural biotechnology journal · 2026Article
- ProtoMol: enhancing molecular property prediction via prototype-guided multimodal learning.Briefings in bioinformatics · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- MetaboGNN: predicting liver metabolic stability with graph neural networks and cross-species data.Journal of cheminformatics · 2025Article
- ACES-GNN: can graph neural network learn to explain activity cliffs?Digital discovery · 2025Article
- Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Explainable machine learning is increasingly used in drug discovery to help rationalize compound property predictions. Feature attribution techniques are popular choices to identify which molecular substructures are responsible for a predicted property change. However, established molecular feature attribution methods have so far displayed low performance for popular deep learning algorithms such as graph neural networks (GNNs), especially when compared with simpler modeling alternatives such as random forests coupled with atom masking. To mitigate this problem, a modification of the regression objective for GNNs is proposed to specifically account for common core structures between pairs of molecules. The presented approach shows higher accuracy on a recently-proposed explainability benchmark. This methodology has the potential to assist with model explainability in drug discovery pipelines, particularly in lead optimization efforts where specific chemical series are investigated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.