ArticleJournal of cheminformatics2025
VN-EGNN: E(3)- and SE(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification.
Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- 3D Geometric Equivariant Graph Neural Networks with Contrastive Learning for Protein-Ligand Binding Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2.Nature methods · 2026Article
- Extending the range of graph neural networks with global encodings.Nature communications · 2026Article
- Introducing Virtual Points in Equivariant Networks by Extending Atom Representation for Effective Prediction.Journal of chemical theory and computation · 2025Article
- PrankWeb 4: a modular web server for protein-ligand binding site prediction and downstream analysis.Nucleic acids research · 2025Article
- Comparative evaluation of methods for the prediction of protein-ligand binding sites.Journal of cheminformatics · 2024Article
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Authors and funding
6 authors.
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Abstract
We present VN-EGNN, a novel approach to binding site identification that significantly advances predictive performance. By integrating virtual nodes into E(n)- and SE(n)-equivariant graph neural networks (EGNNs) and extending the message-passing scheme, we address limitations of traditional GNNs in modeling complex geometric entities such as binding pockets and at the same time get neural representations of binding sites. Our extensive experiments demonstrate that VN-EGNN sets a new state-of-the-art in locating binding site centers on the COACH420, HOLO4K, and PDBbind2020 datasets, showcasing a marked improvement in the DCC/DCA success rates over existing methods. These results underscore the potential of VN-EGNN in drug discovery and protein-ligand interaction studies.
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Registered trials
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