ArticleJournal of cheminformatics2022
Application of deep metric learning to molecular graph similarity.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- GNN-MA: Soft Molecular Alignment with Cross-Graph Attention for Ligand-Based Virtual Screening.Molecules (Basel, Switzerland) · 2026Article
- MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures.Nature communications · 2025Article
- GESim: ultrafast graph-based molecular similarity calculation via von Neumann graph entropy.Journal of cheminformatics · 2025Article
- From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.Molecular informatics · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular graph similarity based on distance between learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. Finally, we compare the applications of the embedding to standard practices, with a focus on known failure points and edge cases; concluding that our approach can be used in conjunction to existing methods.
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Registered trials
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